--- type: Article title: "H3Act: Automated Measuring Semantic Conversion Anomalies of HTTP/3-to-HTTP/1.1 Translation in CDNs (Conference copy)" description: Presents H3Act, which generates structured HTTP/3 probes and analyzes their HTTP/1.1 translation at CDN backends. RFC retrieval, an attack corpus and generator/analyzer feedback guide testing; compressed payload representations and batch summaries support repeated investigation of semantic conversion anomalies. resource: "https://www.usenix.org/conference/usenixsecurity26/presentation/peng-qihang" tags: [article, webseclist-reference, usenix, http3, http, parser-differential, cdn, fuzzing, llm] generated: by: webseclist-refs/1 at: "2026-09-13T22:14:24+00:00" verified: - by: AI archive validation at: 2026-09-13 status: stable stale_after: 2027-09-13 sources: - id: original resource: "https://www.usenix.org/conference/usenixsecurity26/presentation/peng-qihang" title: "H3Act: Automated Measuring Semantic Conversion Anomalies of HTTP/3-to-HTTP/1.1 Translation in CDNs (Conference copy)" author: Qihang Peng, Siyuan Tian, Yongxin Qiu, Jinyang Huang, Yaru Yang, Xiang Li, Jia Zhang, Yiming Zhang, Haixin Duan, Yunsenxiao Lin, Shugen Chen, Liqun Yang also_at: - "https://www.usenix.org/system/files/usenixsecurity26-peng-qihang.pdf" authors: - Qihang Peng - Siyuan Tian - Yongxin Qiu - Jinyang Huang - Yaru Yang - Xiang Li - Jia Zhang - Yiming Zhang - Haixin Duan - Yunsenxiao Lin - Shugen Chen - Liqun Yang canonical_url: "" cited_by: - "2026-ai.md:158" commit: "" content_sha256: ac2f385fb9ceb6b4353a07fe59bf5b6b502810490b324ac3645efa07a2b8c2d8 depth: full depth_reason: default kind: article language: "" licence: unknown original_url: "https://www.usenix.org/conference/usenixsecurity26/presentation/peng-qihang" published: "" publisher: USENIX publisher_english: "" raw_sha256: 3662b75fec3c9ecbeba89d58bc3d3f27729217fe3612737eee3e8c56f391ba22 retrieved_from: "https://www.usenix.org/system/files/usenixsecurity26-peng-qihang.pdf" retrieved_kind: live retrieved_utc: "2026-09-13T22:14:24+00:00" slug: usenix-h3act-automated-measuring-semantic-conversion-anomalies-http-3-http-copy snapshot: "" title_english: "" translation_file: "" translation_of: "" --- # H3Act: Automated Measuring Semantic Conversion Anomalies of HTTP/3-to-HTTP/1.1 Translation in CDNs (Conference copy) **H3Act: Automated Measuring Semantic Conversion Anomalies of HTTP/3-to-HTTP/1.1 Translation in CDNs (Conference copy)** - Qihang Peng, Siyuan Tian, Yongxin Qiu, Jinyang Huang, Yaru Yang, Xiang Li, Jia Zhang, Yiming Zhang, Haixin Duan, Yunsenxiao Lin, Shugen Chen, Liqun Yang, USENIX. - Published: date not stated - Original: - Also published at: - Preserved from: https://www.usenix.org/system/files/usenixsecurity26-peng-qihang.pdf (live) on 2026-09-13 - Licence: unknown Rights remain with the original author and publisher. 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H3Act: Automated Measuring Semantic Conversion Anomalies of HTTP/3-to-HTTP/1.1 Translation in CDNs Qihang Peng and Siyuan Tian, Institute for Network Sciences and Cyberspace, BNRist, Tsinghua University; Yongxin Qiu, Beihang University; Jinyang Huang, Central South University; Yaru Yang, Institute for Network Sciences and Cyberspace, BNRist, Tsinghua University; Xiang Li, Nankai University; Zhongguancun Laboratory; Jia Zhang, Institute for Network Sciences and Cyberspace, BNRist, Tsinghua University; Zhongguancun Laboratory; Yiming Zhang, Institute for Network Sciences and Cyberspace, BNRist, Tsinghua University; Haixin Duan, Institute for Network Sciences and Cyberspace, BNRist, Tsinghua University; Quancheng Laboratory; Yunsenxiao Lin and Shugen Chen, Tencent Technology Co., Ltd.; Liqun Yang, Beihang University https://www.usenix.org/conference/usenixsecurity26/presentation/peng-qihang This paper is included in the Proceedings of the 35th USENIX Security Symposium. August 12–14, 2026 • Baltimore, MD, USA ISBN 978-1-939133-58-8 Open access to the Proceedings of the 35th USENIX Security Symposium is sponsored by H3Act: Automated Measuring Semantic Conversion Anomalies of HTTP/3-to-HTTP/1.1 Translation in CDNs Qihang Peng1 , Siyuan Tian1 , Yongxin Qiu2 , Jinyang Huang3 , Yaru Yang1 , Xiang Li4,5, , Jia Zhang1,5, , Yiming Zhang1 , Haixin Duan1,6 , Yunsenxiao Lin7 , Shugen Chen7 , Liqun Yang2 1 Institute for Network Sciences and Cyberspace, BNRist, Tsinghua University, China 2 Beihang University, 3 Central South University, 4 Nankai University 5 Zhongguancun Laboratory, 6 Quancheng Laboratory, 7 Tencent Technology Co., Ltd. Abstract significantly expand the attack surface. Specifically, in the context of HTTP/2-to-HTTP/1.1 translation, both academia Content Delivery Networks (CDNs) are adopting HTTP/3 and industry have identified critical vulnerabilities, such as to enhance performance; however, they often need to con- request smuggling and bandwidth amplification [24, 28, 32]. vert it to HTTP/1.1 for compatibility. This conversion creates significant attack surfaces and may reintroduce confirmed or While HTTP/3 adoption is accelerating due to the perfor- even patched vulnerabilities in HTTP/2 or HTTP/1. Unfor- mance benefits of QUIC, its security posture within CDN tunately, existing tools struggle to adapt to the HTTP/3 envi- protocol translation scenarios remains underexplored. Al- ronment and efficiently leverage accumulated attack knowl- though HTTP/3 maintains high semantic consistency with edge. To overcome these challenges and systematically mea- HTTP/2 [6, 16, 57], this similarity often fosters a false sense sure HTTP/3-to-HTTP/1.1 conversion anomalies within the of security. Developers may assume that security flaws al- black-box CDN environment, we present H3Act, a dual-agent, ready patched in the HTTP/2-to-HTTP/1 translation are in- knowledge-driven fuzzing framework targeting HTTP/3-to- herently absent in the HTTP/3-to-HTTP/1 context. However, HTTP/1.1 semantic conversion anomalies. Our approach com- the migration from TCP to QUIC requires a fundamental bines Large Language Models (LLMs) with Hybrid Retrieval- reimplementation of the protocol stack. This contradiction, Augmented Generation (RAG) to automatically transform arising from semantic inheritance through implementation re- protocol specifications and historical threat intelligence into construction, may cause new code to fail to inherit historical high-precision HTTP/3 test payloads, enabling regression security patches, thereby regressing known protocol semantic testing of semantic translation risks. In a large-scale study of conversion anomalies. The regression of old attacks is not 9 commercial CDNs, including Cloudflare, Cloudfront, and an exaggeration, as demonstrated by previous studies [7, 48]. Tencent CDN, we found a significant regression in protocol Therefore, a systematic measurement of HTTP/3 security in security, which means vulnerabilities in old protocols are real-world CDN deployments is urgently needed. reintroduced in HTTP/3. Our research identified 7 common However, assessing this risk presents unique challenges. attack vectors across various categories, including request First, new mechanisms introduced in HTTP/3 hinder the di- smuggling, cache poisoning, and Denial-of-Service (DoS) rect migration of existing attack payloads from HTTP/2 or amplification. Every CDN is vulnerable to at least one attack HTTP/1 [7], and transforming the textual knowledge associ- vector. We have responsibly disclosed these vulnerabilities ated with these payloads into usable HTTP/3 test cases incurs and have received confirmations from some vendors. These substantial manual effort [56, 62]. Second, detecting anoma- findings highlight that the CDN ecosystem currently lacks lies in protocol semantic translation requires a deep under- the capacity to maintain security consistency checks while standing of semantics [56, 60, 62, 63]. During detection, tools pursuing improvements in protocol performance. should “understand” request intent to capture subtle logical discrepancies between messages before and after translation. Third, as black-box environments, CDNs provide almost no 1 Introduction additional effective feedback. Traditional rule-based fuzzing methods cannot directly leverage knowledge from documenta- Content Delivery Networks (CDNs), an essential part of In- tion, nor can they effectively capture semantic understanding ternet infrastructure, need to continuously translate between during testing. Consequently, they require significant manual modern protocols (e.g., HTTP/3) and traditional protocols effort to translate knowledge into generation and detection (e.g., HTTP/1.1) to balance performance and compatibility. rules [28, 29, 56, 62, 63]. Meanwhile, white-box or grey-box This translation introduces complex intermediate states that testing lacks feedback signals in CDN environments. USENIX Association 35th USENIX Security Symposium 4901 Large Language Models (LLMs) offer a unique solution plies a high degree of semantic similarity between HTTP/3 to these challenges. They leverage a deep understanding of and HTTP/2 from the outset. However, to prevent confusion, textual knowledge, reasoning capabilities for cross-protocol the IETF decoupled the application and transport layers in semantic mapping, and the creativity to generalize new attack the final specification: QUIC was established as a general- vectors from historical data [40, 41, 62, 64]. LLMs convert purpose transport protocol, while HTTP/3 was defined as the abstract semantic constraints from RFCs and attack primitives application-layer protocol that uses it. from papers and reports into test strategies. They infer internal The IETF released the QUIC standard [26] first, followed translation logic by analyzing differences in messages across by the HTTP/3 standard one year later. Concurrent with RFC black-box environments, thereby systematically exploring 9114, the IETF redefined HTTP semantics in RFC 9110 [16] semantic vulnerabilities within the protocol translation layer. and caching in RFC 9111 [14]. Based on these new specifi- To overcome these challenges and systematically measure cations, the IETF redefined HTTP/1.1 [15] and HTTP/2 [57] HTTP/3-to-HTTP/1.1 conversion anomalies within the black- and formulated HTTP/3. This consolidation resolved the box CDN environment, we propose H3Act, a knowledge- previous fragmentation caused by numerous scattered docu- driven fuzzing framework that targets HTTP/3-to-HTTP/1.1 ments [3,5,12,13,17–20,51–54]. According to Cloudflare [4], semantic conversion anomalies. We employ a dual-agent approximately 21% of global requests directed to their net- fuzzing architecture to generate HTTP/3 test requests and work in 2025 utilize HTTP/3. Furthermore, W3Techs [59] analyze logs to identify conversion anomalies and guide the reports that roughly 36.8% of websites support HTTP/3 as of generation process. To equip agents with the necessary expert late December 2025. knowledge, we implement a hybrid RAG architecture. This Defined in RFC 9000, QUIC is a next-generation transport system statically injects existing attack primitives into the layer protocol. Built upon UDP, QUIC provides reliability system prompt and integrates an external dynamic-retrieval comparable to TCP while effectively resolving the Head- tool, enabling agents to consult sliced and vectorized RFC of-Line blocking issue inherent in TCP, thereby significantly documents. We evaluated the runtime performance and false reducing latency. Additionally, QUIC integrates and mandates positive rate of H3Act. Our results show that H3Act main- the use of TLS 1.3 to encrypt application-layer messages and tains an average false positive rate between 10.3% and 15.8%, protect the content of communications. which falls within an acceptable range. Using this framework, we evaluate 9 commercial CDNs across 7 major CDN providers and identify 7 attack vectors, in- 2.2 CDN Overview cluding DoS, cache poisoning, and request smuggling. Every CDNs are a critical component of modern internet infrastruc- CDN is vulnerable to at least one vector. We have conducted ture. Due to their massive scale and scheduling mechanisms, responsible disclosure; Baidu and Tencent have confirmed CDNs are favored for three benefits: accelerating global con- our findings, while other CDN providers are currently assess- tent delivery, concealing the identities of origin servers, and ing the associated risks and impacts. This study confirms the defending against DoS attacks. According to 6sense’s data [1], resurgence of protocol translation risks in the HTTP/3 era. more than 6 million companies worldwide use CDNs. Contributions. Specifically, our contributions are as follows: The CDN architecture consists of edge nodes and central Additionally, we validate the effectiveness of our architec- nodes, both of which are widely deployed globally [10]. Edge ture through ablation studies. nodes serve as the ingress and egress points of the CDN, 1. We conduct empirical measurements across major com- handling content caching and distribution tasks. Conversely, mercial CDNs, confirming the prevalence of protocol transla- central nodes are responsible for functions such as load bal- tion vulnerabilities and highlighting the challenge of security ancing. Furthermore, CDNs employ scheduling strategies, regression faced by CDN vendors during protocol upgrades. such as DNS-based or anycast-based scheduling, to ensure 2. To conduct these measurements, we propose H3Act, a users access the nearest edge node. When a client accesses a knowledge-driven fuzzing framework that efficiently migrates server proxied by a CDN, the request is routed to the nearest existing protocol-semantic threat intelligence from HTTP/3 ingress node. If the requested data hits the CDN cache and the to HTTP/1.1. replica is valid, the edge node returns the cached copy directly to the client. Otherwise, the CDN performs internal schedul- 2 Background ing to initiate a request to the origin from an egress node (a process known as "back-to-origin"). The data retrieved is then returned to the client via the ingress node, which caches the 2.1 HTTP/3 & QUIC resource for future use. Defined in RFC 9114 [6], HTTP/3 is the third major ver- As demonstrated by this workflow, the globally distributed sion of the Hypertext Transfer Protocol. Its most significant delivery and caching architecture effectively accelerates feature is the transition from TCP to QUIC at the transport global content interaction. Since the client interacts exclu- layer. Initially termed "HTTP/2-over-QUIC," this name im- sively with the CDN, the origin server’s information re- 4902 35th USENIX Security Symposium USENIX Association mains unexposed. Additionally, DoS attack traffic is dis- 3 Methodology of H3Act tributed across multiple CDN nodes for filtering and scrub- bing, thereby significantly mitigating its impact. In this section, we primarily present the threat model and the associated challenges. Subsequently, we expound on the architecture and workflow of H3Act. We further provide an in- depth discussion of H3Act’s two modules: Hybrid RAG and Dual-Agent Fuzzing. Finally, we detail the implementation 2.3 HTTP Attack in CDN of H3Act. Recent studies identify various attacks targeting HTTP en- 3.1 Framework Overview vironments. Based on their techniques and impacts, these attacks fall into three broad categories: Denial of Service 3.1.1 Threat Model and Challenge (DoS), cache poisoning, and request smuggling. We consider an attacker that can impersonate a legitimate The Denial of Service (DoS) attack is focused on making a client and issue HTTP/3 requests to the CDN ingress node resource (site, application, server) unavailable for the purpose without obstruction. The CDN processes these requests nor- it was designed [47]. In CDN environments, attackers typi- mally, modifying and forwarding them in accordance with its cally exhaust the bandwidth between edge nodes and origin standard workflow when forwarding is required. The threat servers (e.g., H2Amp [24], RangeAmp [34], CondAmp [63]) model is illustrated in Figure 1. or consume all available connections (e.g., Slow Attack [24]). The attacker’s capabilities are subject to the following con- This prevents the CDN from retrieving necessary files from straints: 1. The attacker possesses no specific knowledge of the origin, which disrupts normal service delivery. Other vari- the target origin and cannot access it directly. 2. The attacker ants also exist. Attackers may disable routing nodes between cannot access the CDN’s internal network or its source code. the CDN edge and the origin server to block communica- 3. The client can only craft HTTP/3 messages at the semantic tion [24], or redirect users to suboptimal edge nodes to in- layer; it cannot modify the transport layer (QUIC) or lower crease latency or render the service unavailable [25]. layers. 4. The attacker controls a server proxied by the CDN Cache poisoning targets the native caching mechanism of and uses it to poison the CDN cache. CDNs [46]. The goal is to force the CDN to store malicious Attackers aim to compromise the origin through improper responses. If a response is cached in a shared web cache, such semantic translation by the CDN, posing three primary threats: as those commonly found in proxy servers, then all users of DoS, Cache Poisoning and Request Smuggling. that cache will continue to receive the malicious content until Detecting semantic vulnerabilities in this threat model re- the cache entry is purged. The "Host of Troubles" attacks by quires extensive expert knowledge, a deep understanding of Chen et al. [8] demonstrate this technique. Attackers craft protocol semantics, and effective handling of black-box envi- requests that keep the cache key as victim.com, while the ronments. Traditional rule-based or machine-learning-based cache actually fetches and stores a malicious response from fuzzers struggle to meet these requirements, motivating the attacker.com. As a result, all subsequent users requesting use of LLMs in this study. We detail these challenges below. victim.com through this cache receive the malicious content Challenge 1: Difficulty in utilizing expert knowledge. from attacker.com. Substantial attack knowledge applicable to this threat model already exists, though it is often embedded in texts such as Request smuggling exploits parsing inconsistencies across academic papers, blogs, and vulnerability reports. Traditional different servers, particularly regarding request boundaries. fuzzers generally cannot extract knowledge directly from This technique hides additional requests, which security poli- these sources and rely heavily on manual definitions [56, 62]. cies might otherwise block, within a seemingly benign re- In contrast, LLMs possess strong natural language understand- quest [15, 61]. Kettle et al. [31–33] demonstrate several effec- ing capabilities. They can directly get knowledge from text, tive request smuggling techniques with real-world impact. A translate it into HTTP/3 requests, and generalize from existing common approach exploits conflicts between the Content- concepts to discover novel vulnerabilities [40, 41, 62, 64]. Length and Transfer-Encoding headers to trigger parsing Challenge 2: Complexity of semantic definitions and im- discrepancies. Notably, HTTP/2 does not use the Transfer- plementations. HTTP semantics involve complex definitions Encoding header. Therefore, when an HTTP/2 request con- and implementations [56, 60, 63]. Some standards are strictly taining this header is converted to HTTP/1.1, it introduces enforced with zero tolerance for errors. Simple random muta- additional smuggling risks [32]. tion or generation often produces numerous invalid test cases These three categories are not strictly mutually exclusive. that are immediately discarded by parsers, hindering deep test- For instance, attackers can leverage cache poisoning to per- ing. Conversely, LLMs equipped with expert knowledge can form request smuggling (CPDoS) [44], or use request smug- comprehend protocol specifications and abstract rules. This gling to achieve cache poisoning [61]. allows them to construct targeted, malicious samples that pass USENIX Association 35th USENIX Security Symposium 4903 3.2 Hybrid RAG To address the expert-knowledge requirements for fuzzing tasks, we propose a Hybrid RAG. We categorize expert knowl- edge into two types based on differences in timeliness, infor- mation length, and the base model’s prior mastery. Figure 1: The Threat Model of CDN Attacks. Category 1: Attack Primitives. This knowledge is pri- marily scattered across academic papers, technical blogs, and vulnerability advisories. It is characterized by relatively short parser validity checks, thereby enabling more rigorous testing. text length but high value. Furthermore, due to the rapid it- Similarly, in the analysis task, LLMs can ‘understand’ request eration of attack methods, base models struggle to cover the intent. This allows them to precisely capture subtle logical latest attack intelligence during pre-training. Consequently, discrepancies between pre-translation and post-translation their prior mastery of this knowledge is relatively low. messages, thereby identifying anomalies. Category 2: Protocol Specifications. This primarily refers Challenge 3: Adaptability to black-box environments. to RFCs. These documents are lengthy and detailed, includ- CDNs are typically black-box environments, lacking open- ing numerous state machines and error code definitions, yet source code or accessible code coverage data [28, 29, 56, 60, they exhibit greater structure. We consider RFC documents 63]. This may prevent many fuzzers that rely on such infor- to be high-quality corpora that are likely to be included in the mation from functioning correctly, often resulting in blind LLM’s pre-training dataset. While models typically possess searching. However, LLMs with strong logical reasoning ca- a macroscopic understanding of protocols, they are prone to pabilities can better grasp the context of protocol translation. "Factuality Hallucination" when dealing with specific param- They can identify internal translation logic from complex logs eter values, obscure error codes, or edge-case state transitions. and infer potential "translation ambiguities" or mishandling, Based on this analysis, we propose a hybrid strategy: static thereby enabling effective deep testing. injection for attack primitives, and dynamic retrieval for pro- tocol specifications. 3.1.2 Framework and Workflow 3.2.1 Static Injection via System Prompt We implement static injection for attack primitives by embed- To address the challenges, we propose H3Act, a fuzzing frame- ding them into the system prompt. Static injection is well- work based on Hybrid RAG and Dual-agent Fuzzing. It aims suited for these primitives not only because of their brevity to efficiently detect anomalies that arise during the seman- but also because they should remain continuously active dur- tic protocol conversion from HTTP/3 to HTTP/1.1 within ing the process to shape an attack mindset for each genera- CDNs. The overall framework adopts a closed-loop feedback tion. These primitives represent discrete, high-value knowl- approach. The architecture is illustrated in Figure 2. edge units; their value lies in providing transferable attack The architecture comprises two components: Hybrid RAG paradigms rather than descriptive details. Unlike dynamic and Dual-Agent Fuzzing. The Hybrid RAG module aggre- retrieval, which exposes the model to knowledge only during gates data from attack-related papers, blogs, and RFC doc- specific queries and hinders stable mastery, static injection uments to provide the two agents with attack knowledge leverages In-Context Learning. This mechanism allows the and the ability to query specific RFC content. The Dual- model to use these primitives as a reasoning baseline for every Agent Fuzzing module is responsible for case generation and generation. Furthermore, attack primitives demonstrate strong anomaly analysis during the fuzzing and possesses feedback generalization, a CL.TE conflict in HTTP/2 may inspire the guidance capabilities. The framework operates in batch mode construction of inconsistencies between Content-Length to conduct testing against target CDNs. During the prelim- and DATA frame lengths in HTTP/3. This capacity for cross- inary phase, expert knowledge from RAG is injected into scenario transfer requires the knowledge to be resident in the both agents. For each batch, the Generator receives feedback model context rather than retrieved on demand. from the Analyzer and uses it to generate a batch of valid We initially collect papers and blogs containing knowledge HTTP/3 requests. These requests are transmitted to the CDN regarding HTTP attacks and vulnerabilities. This collection by the sender. Meanwhile, the framework captures essential comprises approximately 18 documents [8, 9, 24, 27–34, 36, data from the current batch’s transmission, including client- 37, 42, 43, 48, 56, 63]. During the preprocessing phase, we side and origin-server logs, and submits them to the Analyzer. perform knowledge distillation on all documents. We focus The Analyzer then analyzes the data to identify potential con- exclusively on one key aspect of the described attacks and version anomalies, evaluates the quality of the current batch vulnerabilities: Principle, which is the underlying mechanism. of cases, and proposes directions for further testing, thereby Other information, such as impact scope and background con- guiding the execution of the subsequent batch. text, is omitted from this distillation process. Ultimately, each 4904 35th USENIX Security Symposium USENIX Association Figure 2: The Architecture of H3Act. document is distilled into a list of tuples: [{Attack Name, criteria for tool invocation: when the agent needs to verify Principle}]. The total knowledge content amounts to ap- specific RFC clauses, state machine transition rules, or error proximately 32,000 tokens, which falls well within the context code definitions, it pauses the current generation process and window limits of mainstream LLMs. We integrate this static issues a tool-invocation instruction. Upon receiving the search knowledge into the system prompt. It is injected into the request, the system performs a Top-N similarity search in the model during the initial interaction batch, ensuring it learns vector database and returns the retrieved raw fragments to the domain-specific knowledge about attacks and vulnerabilities agent as tool output. The agent then combines the context with from the outset. this newly acquired, precise knowledge to complete packet construction. 3.2.2 Dynamic Vector Retrieval For long-text RFC protocol specifications and complex attack 3.3 Dual-Agent Fuzzing details, direct static injection results in significant waste of the We decompose the complex fuzzing task into two functionally context window and increases the risk of the model overlook- orthogonal yet closely coordinating agents. ing information. Therefore, we design a dynamic retrieval mechanism based on tool invocation, enabling the agent to 3.3.1 Necessity of Dual-Agent Architecture "consult the manual." The rationale for this design is that, despite having a gen- We first emphasize the necessity of the dual-agent architecture eral understanding of the HTTP/3 protocol, agents may still in H3Act. This design addresses two primary issues: atten- produce factuality hallucinations regarding protocol details. tion dilution from long contexts and conflicting temperature Dynamic retrieval enables the model to access raw RFC frag- requirements across two tasks. ments on demand when precise specifications are required. 1. Attention Dilution: The dilution effect of long con- This approach avoids context dilution from static injection texts motivates our decision to split the system into double and forces the model to ‘consult the manual’ via tool invo- agents. The attention of LLMs degrades as context length cation, significantly reducing the risk of hallucination. Fur- increases [39, 50, 58]. thermore, the hierarchical structure of RFCs (chapters, sub- 2. Temperature Discrepancy across Two Tasks: In sections, clauses) naturally aligns with the semantic chunking fuzzing, the generation task requires a higher temperature to of vector retrieval, enabling dynamic retrieval to balance pre- encourage divergent thinking and creative payload construc- cision and efficiency. tion. Conversely, the analysis task requires a low-temperature, We construct a local vector database using ChromaDB. deterministic setting to interpret logs objectively and rigor- To address the hierarchical structure of RFC documents, we ously. The black box of CDNs reinforces this requirement. As adopt a logic-based slicing strategy. Specifically, we perform the Analyzer only accesses logs from the client and origin, it an initial segmentation of the RFC text by section, format- should perform meticulous comparisons to detect even subtle ting each slice as structured text: {Source, Section ID, anomalies without missing any details. Description, Content}. Additionally, we slice longer text Assigning generation and analysis tasks to separate agents paragraphs into logical segments. We embed these slices and significantly mitigates context accumulation. We find that the distilled attack primitives in the database as well. Generator operates effectively as a stateless entity. In each We encapsulate retrieval as a tool function callable by the batch, its input consists solely of the system prompt and the agent Within the system prompt, we explicitly define the feedback from the previous batch, with all state maintenance USENIX Association 35th USENIX Security Symposium 4905 handled by the Analyzer. Compared to a stateful approach, to the Generator, directing it to refine the attack vector, thereby this allows the Generator to focus more intently on the expert enabling feedback guidance during testing. knowledge in the system prompt and on immediate feedback. This concentrated attention facilitates smoother execution of 3.3.4 Externalized Memory Stream the generation task. Furthermore, the dual-agent setup enables distinct temperature settings optimized for each task. Although task decomposition mitigates some resource con- In contrast, a single-agent architecture forces all conversa- straints, the accumulated interaction history in continuous, tion history into one model, accelerating context accumulation multi-batch fuzzing rapidly exhausts the LLM’s context win- and diluting attention. Consequently, during the same fuzzing dow. To maintain the system’s long-term memory capabilities batch, the single agent performs worse in specific case genera- without degrading performance, we design a State Distillation tion compared to the dual-agent architecture. It also consumes mechanism. This mechanism divides the memory stream into more input tokens, thereby increasing costs. Finally, merg- two distinct components: ing two orthogonal tasks into a single agent inevitably leads 1. Short-term Memory. This component uses a sliding- to ‘role confusion’ and complicates the implementation of window mechanism. It retains full interaction details only for differentiated temperature settings. the most recent batches (e.g., the last 3), ensuring the Agent remains aware of the immediate testing context. 3.3.2 Generator 2. Long-term Memory. At fixed intervals (e.g., every 3 batches), the system triggers a "memory consolidation" opera- The Generator’s duty is to apply divergent thinking to generate tion. The Analyzer is invoked to perform an in-depth summary concrete test cases. It accepts feedback from the Analyzer of past generations and testing histories. They extract a high- as input and, by combining it with learned knowledge from value "Technical Summary". The old raw conversation history RAG, produces executable cases that strictly adhere to the for Generator and Analyzer will be flushed (retaining only HTTP/3 protocol specifications. the initial system prompt) and replaced with this highly con- Given the strict input format requirements of the transmis- densed Technical Summary. This mechanism simulates the sion tool, the Generator should ensure absolute syntactic pre- process of experience accumulation in human experts. It en- cision in its output. Consequently, we constrain the Generator sures that the agent maintains clarity regarding its objectives to adhere to a predefined template. Furthermore, to address is- and direction even after long-duration, multi-batch testing, sues such as truncation or calculation errors common in LLMs thereby avoiding redundant testing and resource waste. when generating ultra-long strings, we introduce an Abstract Syntax Tree (AST)-based Dynamic Payload Generation mech- 3.4 Implementation anism. The Generator is permitted to use Python-style expres- sions (e.g., {{"A"*10000+"B"*50}}) within JSON fields to We implement our framework using Python 3.12.7 [49].In describe repetitive or patterned data. The system intercepts the implementation, we configure the two agents with dis- these expressions before transmission to execute safe local tinct temperatures and prompts: an initial system prompt evaluation and expansion. This mechanism circumvents the and a batch prompt used per batch. Both prompts include LLM’s Context Window Limit, facilitating the construction sections for identity definition, task description, tool usage, of substantial payloads with minimal token consumption. environment configuration, input data explanation, output re- quirements, and task hints. However, the system prompt addi- 3.3.3 Analyzer tionally incorporates statically injected knowledge. For each agent, shared sections between the system and batch prompts The core responsibility of the Analyzer is to infer attack de- remain consistent. This leverages Ollama’s KV Cache mech- tails from a diverse array of interaction logs. It aggregates anism, allowing the agent to reuse precomputed cache data the raw HTTP/3 cases produced by the Generator, client-side and maximize efficiency. logs, and the converted HTTP/1.1 request and response logs For the Generator’s predefined templates, we use Pydantic from the origin. Leveraging its knowledge base, it analyzes to generate a JSON Schema. This schema defines the structure these inputs to identify conversion anomalies that could poten- of HTTP/3 HEADERS and DATA frames, as well as the tially lead to attacks or other risks. By leveraging a moderate commands for issuing a sending pause. We inject this schema temperature setting, the Analyzer can infer potential attack during each generation step, guiding the model to produce a surfaces from subtle discrepancies in protocol behavior. JSON array that describes a complete HTTP/3 request. This Ultimately, the Analyzer generates not only a detailed risk array is subsequently converted into an executable HTTP/3 assessment report identifying which potential attack surfaces request for the sender. If the output violates the JSON Schema, were triggered by the current batch, but also, more impor- the Generator automatically regenerates. tantly, guidance for the subsequent testing batch. This guid- Regarding implementation details, we develop the sender ance serves as a feedback signal, transmitted in a closed loop using the aioquic library to ensure transport-layer correct- 4906 35th USENIX Security Symposium USENIX Association ness during case transmission. Similarly, we use the python NVIDIA GeForce RTX 4090 GPUs (450W TDP, 24,564MB ast module to generate dynamic payloads. VRAM). The second device serves as the origin, responsible Given the limitations of our local experimental environment for receiving forwarded requests from the CDNs and returning and the model’s performance, we select gpt-oss:120b [45] corresponding responses. It utilizes Nginx 1.24.0-2ubuntu7.5 as the base model and deploy it locally via Ollama. The pro- as the web server software and runs on Ubuntu 24.04.3 LTS gram interacts with the model through the Ollama interface. (GNU/Linux 6.8.0-88-generic x86_64). The hardware speci- fications include a single Intel Xeon Silver 4314 CPU (2.40 GHz, 8 cores, 8 threads), 8 GB of RAM, and 10 Gbps of 4 Experiments and Evaluation network bandwidth, with no dedicated GPU configuration. We configured the Nginx logging module to capture received In this section, we first establish the complete experimental HTTP/1.1 requests and sent HTTP/1.1 responses. Note: Due environment for H3Act, including the CDN configuration and to the large size of the target resource (which includes a 29MB the experimental platform settings. Subsequently, we conduct PNG file), we solely record the byte count of the transmitted a comprehensive evaluation of H3Act in this environment, response body, omitting the actual content. The request and covering runtime performance, false-positive rate assessment, response headers are recorded fully. and ablation studies. Finally, we present a qualitative compar- ison of H3Act with other approaches. 4.2 Evaluation of Framework 4.1 Experiment Setup 4.2.1 Runtime performance 4.1.1 Experimental Subject Overview We conducted our evaluation by first assessing basic runtime performance. We identified a primary variable in our archi- We select CDNs based on three criteria: market popularity, tecture: the number of execution batches. We selected three direct registration for individual users, and free or low-cost levels based on various factors: 10, 20, and 30 batches. We pricing. For market popularity, we aim to select widely stud- executed each configuration 10 times, yielding 30 experimen- ied CDNs [9, 22–24, 34, 35, 37, 38, 63]. For pricing, since our tal runs. For all evaluations, we consistently used Cloudflare, work spans a long period and involves multiple rounds of iter- keeping other conditions, such as the knowledge base and ation and testing, for cost control purposes, we expect that the base prompts, constant. We statistically analyzed the system’s services should support monthly or usage-based billing, with various runtime performance metrics, and the averaged results an average monthly cost under $5. Based on these criteria, are presented in Table 1. The metrics are defined as follows: we exclude less popular CDNs, as well as providers such as "Cases Generated" indicates the total number of valid HTTP/3 Akamai, Google, and Microsoft, which either do not allow requests generated per run. "Agent Interactions" refers to the direct individual registration or charge higher fees. Finally, number of interactions with the agent per request, including we selected 9 CDNs across 7 providers that support HTTP/3 calls to dynamic query tools. "Agent Time" represents the client connections: Alibaba CDN, Alibaba ESA, Baidu CDN, total time spent on the agent per run, covering both reasoning Cloudflare, CloudFront, Fastly, Huawei CDN, Tencent CDN, and response generation. "Sum Time" denotes the total exe- and Tencent EdgeOne. Given their significant market share cution time per run. "Tokens (Input)" and "Tokens (Output)" and influence, we believe security vulnerabilities in these ser- indicate the total number of input and output tokens, respec- vices could expose a vast number of general users to threats. tively, across all agents and interactions during a single run. We adopted a principle of minimal modification for CDN By combining these token counts with official API pricing, configurations. We primarily only enabled HTTP/3 function- users who plan to replace H3Act’s base model with alternative ality (which typically requires manual activation). We essen- APIs can estimate the expected cost per run. tially left caching rules, durations, and other optimization set- tings unchanged, except for specific configurations required 4.2.2 Evaluation of False Positive Rate by our test cases, such as enabling Range requests. We evaluated the accuracy of our approach. We conducted 4.1.2 Experiment Platform Setup experiments on all 9 CDNs mentioned in Section 4.1.1. We perform five independent runs for each CDN. Each run com- We establish the experimental platform using two separate prises 10 batches, yielding a total of 45 experiments. We devices. The first device functions as the client, running the collected all evaluation reports from each batch, totaling 450. H3Act and sending requests to the target CDNs. This de- To ensure rigorous evaluation, we defined "a successful vice operates on Ubuntu 22.04.5 LTS (GNU/Linux 6.8.0-87- discovery" of H3Act as the generation of a valid HTTP/3 re- generic x86_64). Its hardware configuration includes dual quest by the Generator, followed by the Analyzer confirming Intel Xeon Gold 6326 CPUs (2.90 GHz, 16 cores, 32 threads), a specific anomaly derived from dual-ended logs. Human ex- 192 GB of RAM, 10 Gbps of network bandwidth, and four perts manually verify whether these high-confidence reports USENIX Association 35th USENIX Security Symposium 4907 Table 1: Runtime Performance of H3Act. 4.2.3 Ablation Study Batch 10 20 30 Cases We investigated the impact of single-agent versus dual-agent 74.4 136.5 215.6 architectures and the inclusion of RAG on our method. Fo- Generated Agent cusing on runtime performance, we selected Cloudflare as the 36.7 72.5 106.1 Interactions experimental subject. We conducted 10 runs of 10 batches Agent Time (s) 1405.2 2922.4 4477.2 each on Cloudflare using two configurations: Single-Agent Sum Time (s) 1903.6 3815.0 5846.2 with RAG and Dual-Agent without RAG. We present the av- Tokens 2068380.0 4291985.0 6633276.0 erage results in Table 3. For all architectures, the generation (Input) Tokens component is stateless. 74567.8 142442.4 222392.7 (Output) Table 3: Runtime Performance of Single-Agent with RAG and Dual-Agent without RAG. correspond to genuine anomalies. Specifically, three human Dual-Agents Single-Agent Dual-Agents experts grade the outputs. When an alert fires, they first check Architecture with RAG with RAG without RAG whether an anomaly has already been reported; if not, they Cases 74.4 65.6 86.6 examine the corresponding HTTP/3 request, the translated Generated HTTP/1.1 request observed at the origin, and the logs. For Agent 36.7 33.6 34.0 skilled experts, reviewing a 10-batch run (approx. 70 requests Interactions Agent Time (s) 1405.2 1353.2 1520.8 as in Table 1) takes only about 5 minutes. Sum Time (s) 1903.6 1879.7 2141.1 We used the False Positive Rate (FPR) as our metric. We Tokens 2068380.0 2200551.0 843118.0 defined FPR as the ratio of manually confirmed false alarms (Input) Tokens to the total number of anomalies reported by the model in a 74567.8 58027.2 70596.6 (Output) single experiment. The final result is the average of the five runs. We did not investigate whether false positives stem from agent hallucination or other causes. We treated all instances Regarding the number of agents, we observed that the equally, relying solely on the reported results. single-agent architecture offers no significant advantage over the dual-agent architecture in terms of average generation time per sample or output token cost. However, its input cost Table 2: FPR and Discovered Anomalies of H3Act running is significantly higher. on CDNs. We also evaluated the FPR of these architectures. To reduce Avg. Avg. Avg. Total Avg. workload, we selected the Huawei CDN and the Tencent CDN CDN Generated Unique Unique TTFF as targets. The experimental methodology and evaluation FPR Cases Anomalies Anomalies (Batch) Alibaba metrics remain consistent with Section 4.2.3. The results, as 72.8 4.2 11.4% 12 1.4 CDN shown in Table 4, indicate that both the dual-agent structure Alibaba and RAG reduce the false-positive rate and, to some extent, 74.0 3.6 10.3% 10 1.6 ESA Baidu 76.2 4.0 12.5% 10 2.8 increase the number of detected anomalies. Cloudflare 79.8 3.0 11.0% 8 2.2 Cloudfront 75.8 3.4 15.8% 7 4.0 Fastly 74.8 4.0 12.4% 11 1.2 Table 4: FPR and Discovered Anomalies of Two Architectures Huawei 72.0 4.2 12.6% 13 2.0 Tencent running on CDNs. 71.6 3.8 14.0% 10 1.6 EdgeOne Tencent Avg. Avg. Total Avg. 74.6 3.8 10.8% 13 1.2 Avg. CDN CDN Architecture Generated Unique Unique TTFF FPR Cases Anomalies Anomalies (Batch) Dual-Agent 72.0 4.2 12.6% 13 2.0 with RAG Huawei Single-Agent We present the results in Table 2. The method maintains a with RAG 63.2 3.4 14.2% 9 2.0 high discovery capability, identifying an average of 3-4 unique Dual-Agent 78.6 2.4 30.6% 7 2.4 without RAG anomalies per batch. Simultaneously, it has a false-positive Dual-Agent 74.6 3.8 10.8% 13 1.2 rate ranging from 10.3% to 15.8%. Additionally, Time-to- Tencent with RAG First-Failure (TTFF) data indicates that the method typically CDN Single-Agent 68.6 3.6 13.2% 8 1.6 with RAG detects the first anomaly within the first three batches, demon- Dual-Agent 88.6 2.2 20.7% 5 2.2 strating its detection efficiency. without RAG 4908 35th USENIX Security Symposium USENIX Association 4.2.4 Comparison of Other Frameworks This study does not directly compare with existing tools. In the black-box CDN environment, traditional coverage metrics are unavailable. Moreover, the number of discovered vulnera- bilities depends heavily on the target implementation, making objective quantitative comparisons difficult. Here, we provide (a) Attack with Single Range a brief qualitative comparison of H3Act against existing tools, including Reqsminer [63], HDiff [56], and Frameshifter [28]. We conduct an in-depth analysis of the theoretical design and implementation of these tools. We consider H3Act to possess the following advantages over existing approaches: 1. Input Flexibility: H3Act can fuzz directly from textual (b) Attack with Multiple Range knowledge. In contrast, Reqsminer and HDiff require muta- tions to begin with HTTP semantics defined by ABNF rules, whereas Frameshifter requires mutations based on complete HTTP/2 request frame sequences. 2. Feedback Guidance: H3Act possesses feedback guidance during testing. Conversely, HDiff and Frameshifter lack feed- (c) Attack with Wrong Range back guidance for mutation, relying essentially on random generation. Although Reqsminer incorporates CDN response Figure 3: RangeAmp when CDNs remove Range. states for feedback, its guidance remains limited. Removal: The CDN removes the Range header during trans- 5 Measurement and Findings lation. 4. Ignoring: The CDN completely ignores the Range header. 5. Refusing: The CDN refuses the request. We summarize the HTTP/3-to-HTTP/1.1 conversion anoma- Among these behaviors, Removal poses a severe risk of lies detected during the evaluation and identify 7 distinct HTTP amplification attacks. An attacker can request minimal attack vectors across 9 CDNs, as shown in Table 5. We also data via HTTP/3 while forcing CDN to retrieve the entire re- measure the occurrence of these conversion anomalies in source from origins, potentially exhausting origin bandwidth HTTP/2-to-HTTP/1.1. and disrupting normal service for legitimate users, as shown in Figure 3. Expansion similarly introduces risks of ampli- fication; however, its amplification factor is lower than that 5.1 Denial-of-Service Bugs of Removal, as the CDN requests only finite resource frag- 5.1.1 RangeAmp (Range-Header-based Attack) ments from the origin. Forwarding carries the risk of CPDoS when handling Invalid Range requests. This occurs because Our research reveals that most CDNs exhibit flaws in pro- the origin will return a 416 error, which the CDN may cache, cessing Range headers. Attackers can exploit these flaws by thereby denying access to the resource for normal clients. constructing syntactically valid yet malicious Range requests We summarize the behaviors of each CDN in Table 6. to launch HTTP amplification attacks against the origin. The results show that nearly all tested CDNs exhibit at least To evaluate the behavior of CDNs, our testing covers three one anomalous behavior. According to Li et al [34], Al- typical Range header types: 1. Single Range: Requests a sin- ibaba, Huawei, and CloudFront have fixed this vulnerability gle, small byte range. 2. Multiple Range: Requests multiple in HTTP/1.1, but it has regressed in HTTP/3. We will show overlapping byte ranges. 3. Invalid Range: Requests a byte the amplification factors of the attack in Appendix A. range with logical errors (e.g., 100-90). According to RFC specifications [12,16], the ideal handling 5.1.2 CondAmp (Conditional-Request-based Attack) logic for these requests should be: direct forwarding, forward- ing after merging overlapping ranges, and rejecting at the In our research, we also observe flaws in the processing logic edge node, respectively. However, practical testing indicates of CDNs regarding Conditional Requests. Attackers can ex- that CDNs in the wild do not fully adhere to these specifica- ploit compliant conditional request headers to construct ma- tions. We categorize observed CDN behaviors as follows: 1. licious traffic, thereby inducing HTTP amplification attacks Forwarding: During translation, the CDN remains the Range against the origin. header unchanged, regardless of its validity. 2. Expansion: Our testing covers five types of conditional request The CDN extends the requested range during translation. 3. headers: If-Match, If-None-Match, If-Modified-Since, USENIX Association 35th USENIX Security Symposium 4909 Table 5: Attack Overview Alibaba Alibaba Tencent Tencent Attack Vector Baidu Cloudflare Cloudfront Fastly Huawei CDN ESA CDN EdgeOne RangeAmp [34] ✓ ✓ ✓ ✓ ✓ ✓ ✓ CondAmp [63] ✓ ✓ ✓ ✓ ✓ ✓ ✓ HTTP Slow Attack [24] ✓ ✓ ✓ ✓ ✓ ✓ ✓ URI-Based CP [8] ✓ ✓ Multi-Host-Based CP [8] ✓ ✓ Foo-CRLF Attack ✓ Meta Characters [36] ✓ ✓ ✓ ✓ ✓ Table 6: Behaviors of CDN when dealing Range Headers. mains the If-Range header but expands the requested Range within the associated Range header during translation. The Single Range Multiple Range Wrong Range implications of these behaviors are similar to those described Alibaba Expansion Expansion1 CDN (512k) (512k) Removal in Section 5.1.1. When a CDN adopts the Removal behavior, Alibaba Expansion Expansion1 the workflow by which an attacker exploits this mechanism Removal to launch an amplification attack against the origin server is ESA (512k) (512k) Baidu Other Behaviors2 illustrated in Figure 4. Cloudflare Removal Removal Removal Table 7 presents our observations regarding CDN process- Expansion Expansion3 ing behaviors. All tested CDNs exhibit flaws in the processing Cloudfront Ignoring (1M) (1M) logic for at least one type of conditional request. We will show Fastly Removal Ignoring Ignoring the amplification factors of the attack in Appendix A. Expansion Expansion Huawei Ignoring (512k) (512k) Tencent Expansion4 Expansion4 5.1.3 HTTP Slow Attack Refusing EdgeOne (4k) (4k) Tencent Expansion4 Because our architecture supports constructing HTTP/3 re- Forwarding Refusing CDN (4k) quests with active transmission pauses, we can observe the 1 Aliyun CDN and Aliyun ESA process only the first byte range specified, ignoring impact of HTTP/3 slow-rate attacks on CDNs. This study all subsequent ranges. 2 Baidu CDN initially issues a request for the first 1MB of the resource. It subse- primarily identifies two categories of slow attack vectors: quently forwards the request containing the original headers to the origin server. 1. Slow Headers (Pre-Headers): Transmitting complete or The response received by the client appears normal. 3 Cloudfront merges multiple byte ranges into a single contiguous range then partial HTTP/3 headers to CDN edge nodes and maintaining expands it. connection liveness without executing other operations . This 4 Tencent CDN decomposes a Range request containing multiple byte ranges into aims to determine whether the CDN establishes and sustains multiple separate requests, each specifying a single byte range. Furthermore, each requested range is expanded to 4KB before forwarding. Tencent EdgeOne a persistent connection with the origin. behaves similarly but merges ranges in 4KB units. Consequently, a single back- 2. Slow Body: Transmitting headers that either to-origin request may satisfy multiple client Range requests. lack a Content-Length or contain an excessively big Content-Length to CDN edge nodes, followed by the transmission of the message body at an extremely low If-Unmodified-Since, and If-Range. For the first four rate (e.g., one byte every 3 to 10 seconds). This serves to headers, we constructed test cases where the preset condi- test whether the CDN continuously occupies connection tions are not met (i.e., edge nodes should theoretically re- resources with the origin. turn lightweight responses, such as 304 or 412 status codes). Regarding request method coverage, our testing framework CDNs should either intercept these requests at the edge or supports both POST and GET. Table 8 summarizes the defen- transparently forward the original conditions to the origin sive effectiveness of CDNs against these attacks. The data for verification. For the If-Range header, we constructed indicate that the vast majority of CDNs are susceptible to at standard requests with the expectation that the CDN would least one of the aforementioned slow attack vectors and fail forward them without modifying the header values. to prevent the exhaustion of connection resources effectively. We categorize the CDN processing behaviors for condi- tional request headers as follows: 1. Forwarding: The CDN 5.2 Cache Poisoning Bugs remains the conditional request header unchanged during translation. 2. Removal: The CDN removes the conditional We extend our research to multi-domain environments and ob- request header during translation. 3. Ignoring: The CDN com- serve that certain CDNs exhibit consistency issues during the pletely ignores the conditional request header. 4. Expansion: parsing and conversion of HTTP/3 requests, thereby creating Specifically for If-Range requests, the CDN correctly re- cache-poisoning vulnerabilities. 4910 35th USENIX Security Symposium USENIX Association Table 7: Behaviors of CDN when dealing Conditional Request Headers. If-Match If-None-Match If-Modified-Since If-Unmodified-Since If-Range Alibaba CDN Forwarding Removal Removal Forwarding Forwarding Alibaba ESA Forwarding Removal Removal Forwarding Expansion (512k) Baidu Other Behavior1 Cloudflare Ignoring Removal Removal Ignoring Removal Cloudfront Forwarding Removal Removal Forwarding Expansion (1M) Fastly Forwarding Removal Removal Forwarding Removal Huawei Removal2 Removal2 Removal2 Removal2 Expansion (512k) Tencent EdgeOne Forwarding Ignoring Ignoring Forwarding Expansion (4k) Tencent CDN Forwarding Ignoring Ignoring Forwarding Forwarding 1 Baidu CDN initially issues a request for the first 1MB of the resource. It subsequently forwards the request containing the original headers to the origin. The response received by the client appears normal. 2 Huawei CDN requests only the first 512 KB of the resource in the origin request. Table 8: Whether CDN is Susceptible to the Specific Attack. 5.2.2 Multi-Host-Based Pre Headers Slow Body Pre Headers Slow Body Beyond URI parsing issues, we identified vulnerabilities con- (POST) (POST) (GET) (GET) cerning the handling of duplicate or conflicting :authority Alibaba CDN YES YES NO NO headers. This issue was exclusively detected in Tencent CDN Alibaba and EdgeOne, with the attack scenario illustrated in Figure 6. YES YES YES YES ESA Our experiments reveal that these CDNs accept requests Baidu YES YES YES YES containing multiple Host headers, multiple :authority Cloudflare NO NO NO NO Cloudfront YES YES NO NO headers, or a combination of both. Crucially, these CDNs Fastly NO NO NO NO adopt a "Last-one-wins" strategy for routing decisions. Em- Huawei NO YES NO YES pirical data indicates that when a request contains conflict- Tencent ing headers (e.g., sending :authority: test-tencentcdn YES YES YES YES EdgeOne Tencent followed by :authority: test-tencentcdn-2), the trans- YES YES YES YES lated HTTP/1.1 request utilizes the latter header (in this CDN example, the Host field of the translated request is set to test-tencentcdn-2). If the CDN’s caching mechanism uses the first header to generate the cache key while the rout- 5.2.1 URI-Based ing logic relies on the last, this parsing discrepancy will di- rectly facilitate cache poisoning attacks. According to Chen We observe flaws in the processing logic of absolute URIs et al [8], Tencent has fixed this vulnerability in HTTP/1.1, but within the :path of HTTP/3 messages across certain CDNs. it has regressed in HTTP/3. Our tests demonstrate that when Huawei CDN and Tencent CDN generate back-to-origin HTTP/1.1 requests, if the do- 5.3 Request Smuggling Bugs main information in the :path conflicts with that in the :authority, the system prioritizes the information within 5.3.1 Foo-CRLF Attack the :path field. Consequently, this value is used to generate We identify a critical CRLF injection vulnerability within the Host header of the back-to-origin HTTP/1.1 message. Huawei CDN, stemming from insufficient input validation The attack is illustrated in Figure 5. during the HTTP/3-to-HTTP/1.1 conversion process. Specifically, an attacker can construct a malicious mes- Experiments reveal that Huawei CDN fails to filter CRLF sage specifying the victim domain (victim.com) in the sequences embedded within custom header values. As illus- :authority header, while using an absolute URI pointing to trated in Figure 7, an attacker can inject a payload containing the attacker’s server (e.g., https://attacker.com/) in the bar\r\n\r\n ... into the value of a Foo header within an :path header. The CDN’s protocol translation layer forwards HTTP/3 request. the request to attacker.com. Since the cache key is typi- When the CDN converts the request to HTTP/1.1, it fails cally generated based on :authority, the malicious content to strictly validate CRLF sequences, allowing them to persist returned by the attacker’s server is erroneously cached under in the translated request. According to the HTTP/1.1 spec- the victim domain, thereby achieving cache poisoning. Ac- ification, a double CRLF sequence signifies the end of the cording to Chen et al [8], Tencent has fixed this vulnerability request [15]; thus, the injected CRLF sequence prematurely in HTTP/1.1, but it has regressed in HTTP/3. terminates the current request. Consequently, the origin in- USENIX Association 35th USENIX Security Symposium 4911 (a) Attack with If-Match header Figure 6: Workflow of Multiple-Host-Based Cache Poisoning. (b) Attack with If-None-Match header a malicious request following the double CRLF sequence. Upon conversion, this payload is parsed by the origin as an independent request, thereby enabling HTTP request smug- gling. Furthermore, by specifying Host: attacker.com in the smuggled request, the attacker can induce the CDN to cache a malicious response from the attacker’s server un- der the victim’s domain, thereby achieving cache poisoning (c) Attack with If-Modified-Since header. against legitimate users. 5.3.2 Meta Characters We analyzed the meta character filtering mechanisms across various CDNs and discovered that certain providers exhibit overly lenient handling of non-ASCII and control characters (d) Attack with If-Unmodified-Since header. (meta characters). This issue affects Alibaba CDN, Alibaba ESA, Huawei CDN, Tencent CDN, and Tencent EdgeOne. Specifically, these CDNs forward directly dangerous meta characters, such as \f, \a and \b, from HTTP/3 requests to the HTTP/1.1 origin without performing any encoding, sanitization, or rejection. Since HTTP parsers differ in their interpretation of control characters (e.g., some parsers may treat \f as line terminators or delimiters), this behavior may (e) Attack with If-Range header. create a high risk of triggering request smuggling variants tailored to specific backend implementations (such as specific Figure 4: CondAmp when CDNs remove conditional headers. versions of HAProxy [11]). 5.4 HTTP/2 vs HTTP/3 We manually test the HTTP/2 implementations of CDNs. The result is shown in Appendix B. Our results show that cer- tain issues observed in HTTP/3 are mitigated or absent in HTTP/2, such as Meta Characters. Conversely, some issues appear more frequently in HTTP/2 than in HTTP/3, such as Multi-Host-Based CP. These findings suggest that the HTTP/2 and HTTP/3 protocol stacks in CDNs are implemented inde- pendently and orthogonally. Figure 5: Workflow of URI-Based Cache Poisoning. We also observe broader behavioral differences across CDNs when handling the same requests sent via HTTP/2 and HTTP/3. For instance, HTTP/3 stacks tend to reject re- terprets the residual data as the beginning of a subsequent quests containing extra spaces or uppercase letters in header request. By exploiting this behavior, an attacker can inject fields, while HTTP/2 stacks are more tolerant, either filtering 4912 35th USENIX Security Symposium USENIX Association 2. Validate Conditional Headers: We recommend that CDNs prioritize validating conditional request headers. If the condition allows for a local response, it should be returned immediately without contacting the origin. If interaction with the origin is necessary, the conditional request fields should be preserved and forwarded to the origin. (a) Foo-CRLF Request Smuggling Attack. 3. Disable Streaming Mode for Upstream Connections: We advise against using "streaming mode" [24,28], where the CDN initiates a connection to the origin before receiving the complete client request. We recommend that CDNs wait until the full request is received and buffered before establishing communication with the origin. 4. Enforce Strict Absolute URI Checks: We recommend that CDNs strengthen absolute URI validation. Specifically, if there is a discrepancy between the address specified in the absolute URI and the domain information in the header fields, (b) Foo-CRLF Cache Poisoning Attack. the request should be immediately rejected [8, 15, 16, 19]. 5. Restrict Duplicate Headers: We recommend enhanc- Figure 7: Workflow of Foo-CRLF Attack. ing checks for duplicate header fields. Requests containing duplicates of headers that should hold a unique value (such as Host) should be directly rejected [8, 15, 16, 19]. out extra spaces or converting uppercase letters to lowercase. 6. Filter CRLF and Meta-characters: We recommend HTTP/3 stacks often reject requests that include a host field that CDNs enforce stricter filtering of CRLF sequences and (with correct syntax, simply replacing :authority with host), other meta-characters within header fields to prevent these whereas HTTP/2 stacks typically accept such requests. Ad- anomalies from reaching the origin [15, 19, 44]. ditionally, when rejecting malformed requests, some CDN Although conversion between old and new protocols intro- HTTP/3 stacks terminate the QUIC connection directly with- duces new risks, we do not recommend that service providers out returning an HTTP response with an error status code. withdraw from or avoid adopting new protocols such as Even when error codes are returned, they might differ from HTTP/3. Nor do we advise them to aggressively phase out those returned by the HTTP/2 stack. legacy protocols like HTTP/1.1. The former approach may Overall, these observations support the conclusion that stifle community innovation and hinder the development of HTTP/2 and HTTP/3 protocol stacks are implemented as the new protocol ecosystem, while the latter may harm the independent and distinct components. This conclusion is fur- interests of certain users. We first urge vendors to address the ther supported by open-source proxy software such as Nginx, security risks introduced by the protocol translation layer. We which also implements its HTTP/2 and HTTP/3 stacks sepa- also believe that vendors can collaborate and build consensus rately. with customers and the broader community. This collabora- tion can drive iterative protocol updates at the appropriate time and under suitable conditions, ensuring the orderly re- 6 Discussion placement of legacy protocols. In this section, we primarily discuss the mitigation solution, the limitations of H3Act and future directions. 6.2 Limitations of Methodology We acknowledge the following limitations in H3Act: 6.1 Mitigation Solution 1. High Operational Cost and Low Speed: Although we have optimized token consumption to the greatest extent To address the identified issues, we propose the following possible, the inference latency of the LLM-based generation mitigation recommendations: process remains significantly higher than that of rule-based 1. Validate Range Headers: We recommend that CDNs binary fuzzers. This inherent latency restricts the throughput first validate the effectiveness of Range header values. Invalid required for large-scale scanning. Furthermore, the volume Range fields should immediately result in a 416 error. Fur- of packets generated per batch is notably lower than that of thermore, single-range requests should be forwarded directly. traditional fuzzers. For multi-range requests, CDNs should merge overlapping 2. Interference from Safety Alignment: Commercial and segments to minimize the volume of data retrieved from the open-source LLMs typically undergo safety alignment train- origin before forwarding [12, 16, 34]. ing to prevent the generation of malicious content. In some USENIX Association 35th USENIX Security Symposium 4913 cases, the model misinterprets the research context as a viola- to use the CDN itself to break its own DoS protection. Mean- tion of safety policies. Consequently, it refuses to construct while, REQSMINER, developed by Zheng et al. [63], system- complex attack payloads (such as request smuggling vectors) atically mines various forwarding inconsistency vulnerabili- or analyze the provided logs. Although rare, we have observed ties through automated fuzzing. this phenomenon. Regarding the caching mechanism of CDNs, attackers have 3. LLM Hallucination: LLMs inevitably produce hallu- also developed methods to compromise availability and confi- cinations. In our false-positive rate evaluation, we observed dentiality. In terms of availability, Nguyen et al. [44] proposed that some false alarms originate from hallucinations within Cache Poisoned Denial of Service (CPDoS), which exploits the analysis agent. Although the overall scale is manageable, semantic discrepancies to force edge nodes to cache error this remains a significant limitation of the approach. pages, thereby blocking legitimate access. Regarding data privacy, Mirheidari et al. [42] systematically quantified the risk of sensitive information leakage caused by Web Cache 6.3 Future Directions Deception (WCD) attacks for the first time. Subsequently, by improving detection methods, they further revealed that Future work can proceed in the following directions: WCD threats are widespread among the Alexa Top 10,000 1. Protocol Coverage Expansion: Our current testing pri- websites and are more severe than previously estimated [43]. marily focuses on HTTP/3-to-HTTP/1.1 conversion. Future These studies indicate that the security boundary of CDNs work can extend protocol coverage in two dimensions: first, faces comprehensive challenges, spanning from the underly- by investigating anomalies in HTTP/3-to-HTTP/2 conversion; ing network infrastructure to upper-layer application logic. and second, by performing a comparative security analysis of HTTP/2 and HTTP/3 implementations under identical cases. 2. Scope Expansion: We aim to extend our testing to in- 7.2 HTTP Security clude various ubiquitous open-source reverse proxies (e.g., Nginx, Apache). This will allow us to measure whether "tra- In the field of HTTP security, inconsistencies between pro- ditional" security issues are resurfacing on a broader scale tocol implementations and specifications are a significant following the upgrade from HTTP/2 to HTTP/3. cause of critical vulnerabilities. Empirical research by Chen et 3. Model Capability Enhancement: Constrained by al. [8] revealed that ambiguities in Host header parsing within cost and other factors, our current testing utilized only gpt- HTTP implementations trigger "Host of Troubles" attacks, oss:120b as base models. However, our architecture is de- leading to cache poisoning and policy bypass. Subsequently, signed to be highly modular and open. We anticipate that James Kettle [31] revived the dormant "request smuggling" integrating more powerful next-generation models, such as attack through groundbreaking research. By systematically Gemini 3 Pro or ChatGPT-5.2, will enable the architecture to exploiting parsing discrepancies between Content-Length and uncover a greater number of novel vulnerabilities. Transfer-Encoding headers, he proposed a reliable method- ology for detection and exploitation. This approach not only bypassed security controls but also enabled large-scale cache 7 Related Work poisoning and session hijacking. To discover these complex parsing discrepancies more ef- 7.1 CDN Security ficiently, researchers have proposed various automated de- tection frameworks. Jabiyev et al. [29] introduced T-Reqs, a As CDNs become critical internet infrastructure, their security grammar-based differential fuzzing tool. By systematically threats are receiving increasing attention. In their respective testing combinations of web servers and proxies, this tool surveys, Ghaznavi et al. [21] and Al-Hamdani et al. [2] sys- uncovered multiple novel request smuggling vectors. To ad- tematically reviewed the threats facing CDNs, emphasizing dress the limitations of relying solely on direct testing, Shen the defense challenges ranging from edge to origin. et al. [56] proposed the HDiff framework. This approach In terms of forwarding and optimization mechanisms, re- creatively combined NLP analysis of RFC documents with searchers have uncovered significant weaknesses. Chen et differential testing, systematically identifying semantic gap al. [9] demonstrated the construction of "forwarding loop" attacks across mainstream HTTP implementations. To over- attacks by exploiting forwarding configuration flaws. As re- come black-box testing bottlenecks, Jabiyev et al. [27] pro- search deepens, utilizing protocol inconsistencies for traffic posed GUDIFU. Utilizing grey-box testing and comprehen- amplification has become a new focus. RangeAmp proposed sive search strategies, it detects deep parsing discrepancies by Li et al. [34], CDN-Convert and BtOAmp discovered by more effectively than existing methods. Lin et al. [37, 38], exploiting back-to-origin policy flaws) all Regarding attack impact and protocol evolution, threats demonstrate that attackers can trigger high bandwidth con- stemming from parsing discrepancies persist despite technical sumption. To counter CDN defenses, Guo et al. [24] proposed advancements. Liang et al. [36] developed the HCache tool tar- several methods, including HTTP/2 bandwidth amplification, geting Web Cache Poisoning (WCP), discovering seven new 4914 35th USENIX Security Symposium USENIX Association attack vectors and over 1,000 vulnerable websites on the inter- CDNs, including DoS, cache poisoning, and request smug- net. With the adoption of HTTP/2, Kettle [32] revealed new gling. The ubiquity of these vulnerabilities indicates that dur- risks introduced when front-end servers downgrade HTTP/2 ing the implementation of next-generation network protocol requests to HTTP/1.1 for forwarding (e.g., H2.CL, H2.TE). stacks, the industry may overlook the complexity of semantic Targeting this protocol translation layer, Jabiyev et al. [28] pro- translation between different protocols. Consequently, secu- posed the Frameshifter tool to identify various attacks caused rity risks that were previously mitigated in the HTTP/1.1 and by HTTP/2-to-HTTP/1 conversion anomalies. Even with the HTTP/2 eras have regressed in the HTTP/3. Our work serves latest HTTP/3 protocol, Pisu et al. [48] demonstrated that not only as an efficient automated testing tool but also under- header validation issues in proxies still lead to request smug- scores a critical imperative: as internet infrastructure evolves, gling risks if RFC specifications are not strictly followed. the crucial process of "protocol translation" demands more rigorous security auditing and compliance verification. We hope this study will galvanize the community to prioritize the 7.3 LLM for Vulnerability Discovery security of CDN protocol translation and drive the adoption of more secure HTTP/3 deployment practices. Recent research increasingly explores the application of Large Language Models (LLMs) in vulnerability discovery and net- work protocol security. Meng et al. [41] proposed CHATAFL, Acknowledgments which integrates LLMs into mutation-based protocol fuzzing. By extracting message grammars, enriching seed corpora, This work is supported by the Deng Feng Fund of the Bei- and guiding state transitions, it significantly improved code jing National Research Center for Information Science and coverage and vulnerability discovery for stateful protocols. Technology. Authors from Nankai University (Prof. Xiang Li) Similarly, Wei et al. [62] introduced NeTestLLM, which au- were supported by the National Natural Science Foundation of tomated network protocol testing by using LLMs to extract China (No. 62502236, No. U25B2025), the Natural Science protocol specifications for test case generation and converting Foundation of Tianjin (No. 24JCQNJC02070), and the Open natural language descriptions into executable test scripts. In Project of National Engineering Laboratory for Technology the specific domain of Web Application Firewalls (WAFs), of Internet Domain Name (No. KF202516). Prof. Yiming Wang et al. [60] presented WAF Manis. This grammar-based Zhang is in part supported by the NSFC #62302258. testing approach automatically discovers protocol-level eva- sion vulnerabilities by generating payload-aware malformed HTTP requests that exploit parsing discrepancies. Liu et Ethical Considerations al. [40] proposed PROMEFUZZ. It guided LLMs by con- structing a knowledge base containing code metadata, API As this study involves security testing against real-world com- documentation, and user usage patterns to generate syntacti- mercial CDN infrastructure, we strictly adhered to responsible cally and semantically correct fuzzing harnesses. Furthermore, research guidelines throughout every stage of the experimen- LLMs are used to extract formal specifications for verification. tal design. Our objective was to ensure no disruption to the Sharma [55] introduced PROSPER, which utilizes LLMs to normal operation of public services and no infringement upon extract protocol Finite State Machines (FSMs) from RFC third-party rights. documents. It achieved this by combining the analysis of 1. Controlled Experimental Environment: All test traffic natural language text with visual artifacts such as state dia- was directed exclusively towards origin and domains regis- grams. Finally, Zheng et al. [64] proposed PARVAL, which tered and controlled by the authors. We never attempted to employed a multi-agent LLM approach to extract and com- access, modify, or interfere with any third-party origin data pare format specifications from both source code and RFCs, or cached content. Our threat model explicitly restricts the at- thereby detecting semantic inconsistencies in protocol parser tacker’s capabilities to possessing a controlled origin, thereby implementations. ensuring clear and safe testing boundaries. 2. Minimized Impact: During the verification of DoS- related vulnerabilities (e.g., amplification and slow-rate at- 8 Conclusion tacks), we strictly limited the traffic rate and the number of concurrent connections (e.g., pauses for 0.5 seconds after In this paper, we present H3Act, through which we evaluated each send). We transmitted only the minimum sample size security during HTTP/3-to-HTTP/1.1 protocol conversion in necessary to verify the vulnerability’s existence and ceased CDN environments. Our research reveals a concerning reality: transmission immediately upon confirming the anomalous despite the significant advancements inherent in the HTTP/3 behavior. This ensured that no denial-of-service or resource- protocol, the translation layer introduced to ensure backward exhaustion attacks occurred at the CDN edge nodes. compatibility has emerged as a novel security weakness. Our 3. Data Privacy Protection: This study involves interac- framework successfully identified 7 attack vectors across 9 tions strictly at the protocol level and does not involve any USENIX Association 35th USENIX Security Symposium 4915 personal user data. All logs generated during the experiments HTTP/3 deployment, improve protocol-translation validation contain only constructed test data and are stored within a practices, and better define security boundaries between CDN controlled local environment. infrastructure and customer-controlled origins. Furthermore, For the vulnerability disclosure process. Overall, we re- during the disclosure process, we make every effort to report ported our findings to 7 major CDN vendors and have re- vulnerabilities to all tested providers and minimize negative ceived active responses and engaged in in-depth interactions impact. with 6 of them: CDN customers may face risks from amplification behav- Tencent: The Tencent security team confirmed the vul- iors, cache inconsistencies, or request-confusion effects un- nerabilities regarding the handling of anomalous paths and der certain deployment conditions. However, disclosure also multiple Host/Authority headers (which could lead to cache enables customers to make more informed deployment de- poisoning). As of now, these issues have been successfully cisions, evaluate whether and how to enable HTTP/3, and patched. Other reported vectors will be addressed as optimiza- strengthen origin-side mitigations and cache-control policies. tions in the future. End users could indirectly experience degraded service Baidu: Upon internal review, Baidu explicitly confirmed availability if such behaviors were abused in practice. Con- the potential real-world harm of the CondAmp and has versely, improved awareness and remediation of protocol- patched this vulnerability. translation weaknesses may contribute to a more secure and Alibaba: The Alibaba security team conducted a detailed stable ecosystem over time. internal evaluation of our four reported attack vectors. They The research and security community benefits from a better pointed out that the traffic amplification effects of Range understanding of HTTP/3-to-HTTP/1.1 translation risks and and Conditional-Request behaviors primarily manifest at the regression patterns in independently implemented protocol origin server, and will not cause the CDN itself to become stacks. At the same time, public discussion of these behaviors unavailable. Therefore, they consider this a Shared Respon- may increase awareness of attack techniques among adver- sibility model, suggesting that appropriate defense and rate- saries. We therefore intentionally omit exploit automation limiting mechanisms should be deployed and configured by details, operational attack tooling, and deployment-specific the customer at the origin side, rather than being directly offensive guidance from the paper and released artifacts. blocked by the CDN edge nodes. We reviewed Alibaba’s rele- We carefully considered the potential risks of public disclo- vant documentation, which describes a back-to-origin chunk sure before publication. Some affected vendors acknowledged size between 512KB and 4MB, but notably lacks clear warn- parts of the reported behaviors and initiated remediation ef- ings to users regarding the potential security risks of band- forts, while others considered the observed behaviors to be width amplification. expected operational trade-offs or shared-responsibility issues. Cloudflare: After weeks of technical interaction, Cloud- Consequently, not all identified risks are fully mitigated at flare fully understood our constructed attack patterns. They the time of writing. However, we nevertheless believe pub- categorized this as expected "cache-fill behavior" and stated lication is justified for several reasons. First, the reported that this architectural design is not unique to Cloudflare. attack vectors are derived from publicly documented HTTP CloudFront: The CloudFront team cited their official De- semantics and previously studied classes of parser-confusion veloper Guide, pointing out that proactively requesting more and translation-layer attacks, rather than from undisclosed bytes than specified by the client is an expected system op- implementation secrets. Second, exploiting these behaviors timization behavior designed to improve performance and in practice still requires nontrivial environmental conditions, cache efficiency. including compatible CDN configurations, origin behaviors, Fastly: Our vulnerability report has been successfully sub- and controlled request construction. Third, public discussion mitted and is currently pending initial review by their security of these findings enables operators and customers to better team. evaluate the risks introduced by heterogeneous protocol trans- Huawei: We have communicated with Huawei for several lation, particularly during HTTP/3 adoption. weeks. Despite we have reported multiple times, the vendor still fails to understand the reported issues. Open Science The primary stakeholders in this work include CDN providers, CDN customers (origin operators), end users, and We have made the entire codebase used in this research open the broader research and security community. Different stake- source. The link is https://github.com/AsadaShino666/ holders may experience both benefits and burdens from dis- H3Act or https://zenodo.org/records/20398181. closure. The repository contains two parts. The first part provides CDN providers may incur operational, remediation, and the full source code for the H3Act tool, including all prompts reputational costs associated with investigating and address- and all modules. Researchers wishing to reproduce the tool ing translation-layer inconsistencies. At the same time, the can copy these directly to a local environment for execution. findings may help providers identify regression risks during However, please note that our code currently supports only a 4916 35th USENIX Security Symposium USENIX Association local Ollama environment and does not include API adapta- [11] CVE. CVE-2019-18277, 2019. https://www.cve. tion; details are provided in the repository’s README. The org/CVERecord?id=CVE-2019-16782. second part comprises simple HTTP/3 request transmission logic and test cases corresponding to the vulnerabilities de- [12] Roy T. Fielding, Yves Lafon, and Julian Reschke. Hy- scribed in Section 5. Users who merely wish to scan CDNs for pertext Transfer Protocol (HTTP/1.1): Range Requests. these vulnerabilities can use this simple logic to send packets RFC 7233, June 2014. and manually verify if the messages received by the origin [13] Roy T. Fielding, Mark Nottingham, and Julian Reschke. match our descriptions. This is also detailed in the README. Hypertext Transfer Protocol (HTTP/1.1): Caching. RFC 7234, June 2014. References [14] Roy T. Fielding, Mark Nottingham, and Julian Reschke. HTTP Caching. RFC 9111, June 2022. [1] 6sense, 2026. https://6sense.com/tech/ content-delivery-network-cdn. [15] Roy T. Fielding, Mark Nottingham, and Julian Reschke. HTTP/1.1. RFC 9112, June 2022. [2] Saja Al-Hamdani and Dujan B. Taha. Security in con- tent delivery networks (cdns): A literature review. In [16] Roy T. Fielding, Mark Nottingham, and Julian F. 2025 International Conference on Computer Science Reschke. HTTP semantics. RFC, 9110:1–194, 2022. and Software Engineering (CSASE), pages 132–139, 2025. [17] Roy T. Fielding and Julian Reschke. Hypertext Transfer Protocol (HTTP/1.1): Authentication. RFC 7235, June [3] Mike Belshe, Roberto Peon, and Martin Thomson. Hy- 2014. pertext Transfer Protocol Version 2 (HTTP/2). RFC [18] Roy T. Fielding and Julian Reschke. Hypertext Transfer 7540, May 2015. Protocol (HTTP/1.1): Conditional Requests. RFC 7232, [4] David Belson. The 2025 Cloudflare Radar Year in Re- June 2014. view: The rise of AI, post-quantum, and record-breaking [19] Roy T. Fielding and Julian Reschke. Hypertext Trans- DDoS attacks, 2025. https://blog.cloudflare. fer Protocol (HTTP/1.1): Message Syntax and Routing. com/radar-2025-year-in-review/. RFC 7230, June 2014. [5] David Benjamin. Using TLS 1.3 with HTTP/2. RFC [20] Roy T. Fielding and Julian Reschke. Hypertext Transfer 8740, February 2020. Protocol (HTTP/1.1): Semantics and Content. RFC 7231, June 2014. [6] Mike Bishop. HTTP/3. RFC 9114, June 2022. [21] Milad Ghaznavi, Elaheh Jalalpour, Mohammad A. [7] Efstratios Chatzoglou, Vasileios Kouliaridis, Geor- Salahuddin, Raouf Boutaba, Daniel Migault, and Stere gios Kambourakis, Georgios Karopoulos, and Stefanos Preda. Content delivery network security: A survey. Gritzalis. A hands-on gaze on HTTP/3 security through IEEE Communications Surveys & Tutorials, 23(4):2166– the lens of HTTP/2 and a public dataset. Comput. Secur., 2190, 2021. 125:103051, 2023. [22] Run Guo, Jianjun Chen, Baojun Liu, Jia Zhang, Chao [8] Jianjun Chen, Jian Jiang, Haixin Duan, Nicholas Weaver, Zhang, Haixin Duan, Tao Wan, Jian Jiang, Shuang Hao, Tao Wan, and Vern Paxson. Host of troubles: Multiple and Yaoqi Jia. Abusing cdns for fun and profit: Security host ambiguities in http implementations. In Proceed- issues in cdns’ origin validation. In 2018 IEEE 37th ings of the 2016 ACM SIGSAC Conference on Com- Symposium on Reliable Distributed Systems (SRDS), puter and Communications Security, CCS ’16, page pages 1–10, 2018. 1516–1527, New York, NY, USA, 2016. Association for Computing Machinery. [23] Run Guo, Jianjun Chen, Yihang Wang, Keran Mu, Bao- jun Liu, Xiang Li, Chao Zhang, Haixin Duan, and Jian- [9] Jianjun Chen, Jian Jiang, Xiaofeng Zheng, Haixin Duan, ping Wu. Temporal CDN-Convex Lens: A CDN- Jinjin Liang, Kang Li, Tao Wan, and Vern Paxson. For- Assisted Practical Pulsing DDoS Attack. In 32th warding Loop Attacks in Content Delivery Networks. USENIX Conference on Security Symposium, 2023. In Proceedings 2016 Network and Distributed System Security Symposium, 2016. [24] Run Guo, Weizhong Li, Baojun Liu, Shuang Hao, Jia Zhang, Haixin Duan, Kaiwen Sheng, Jianjun Chen, and [10] Cloudflare. What is cdn?, 2026. https://www. Ying Liu. CDN Judo: Breaking the CDN DoS Protection cloudflare.com/learning/cdn/what-is-a-cdn/. with Itself. In 27th Annual Network and Distributed USENIX Association 35th USENIX Security Symposium 4917 System Security Symposium, NDSS 2020, San Diego, [34] Weizhong Li, Kaiwen Shen, Run Guo, Baojun Liu, Jia California, USA, February 23-26, 2020. The Internet Zhang, Haixin Duan, Shuang Hao, Xiarun Chen, and Society, 2020. Yao Wang. Cdn backfired: Amplification attacks based on http range requests. In 2020 50th Annual IEEE/IFIP [25] Shuai Hao, Yubao Zhang, Haining Wang, and Angelos International Conference on Dependable Systems and Stavrou. End-Users get maneuvered: Empirical analysis Networks (DSN), pages 14–25, 2020. of redirection hijacking in content delivery networks. In 27th USENIX Security Symposium (USENIX Security [35] Jinjin Liang, Jian Jiang, Haixin Duan, Kang Li, Tao 18), pages 1129–1145, Baltimore, MD, August 2018. Wan, and Jianping Wu. When https meets cdn: A case USENIX Association. of authentication in delegated service. In 2014 IEEE Symposium on Security and Privacy, pages 67–82, 2014. [26] Jana Iyengar and Martin Thomson. QUIC: A UDP- Based Multiplexed and Secure Transport. RFC 9000, [36] Yuejia Liang, Jianjun Chen, Run Guo, Kaiwen Shen, May 2021. Hui Jiang, Man Hou, Yue Yu, and Haixin Duan. In- ternet’s invisible enemy: Detecting and measuring web [27] Bahruz Jabiyev, Anthony Gavazzi, Kaan Onarlioglu, and cache poisoning in the wild. In Proceedings of the 2024 Engin Kirda. Gudifu: Guided differential fuzzing for on ACM SIGSAC Conference on Computer and Commu- http request parsing discrepancies. In Proceedings of the nications Security, CCS ’24, page 452–466, New York, 27th International Symposium on Research in Attacks, NY, USA, 2024. Association for Computing Machinery. Intrusions and Defenses, RAID ’24, page 235–247, New York, NY, USA, 2024. Association for Computing Ma- [37] Ziyu Lin, Zhiwei Lin, Ximeng Liu, Jianjun Chen, Run chinery. Guo, Cheng Chen, and Shaodong Xiao. CDN cannon: Exploiting CDN Back-to-Origin strategies for amplifi- [28] Bahruz Jabiyev, Steven Sprecher, Anthony Gavazzi, cation attacks. In 33rd USENIX Security Symposium Tommaso Innocenti, Kaan Onarlioglu, and Engin Kirda. (USENIX Security 24), pages 5717–5734, Philadelphia, FRAMESHIFTER: Security Implications of HTTP/2-to- PA, August 2024. USENIX Association. HTTP/1 Conversion Anomalies. In Kevin R. B. Butler and Kurt Thomas, editors, 31st USENIX Security Sympo- [38] Ziyu Lin, Zhiwei Lin, Ximeng Liu, Zuobing Ying, and sium, USENIX Security 2022, Boston, MA, USA, August Cheng Chen. Unveiling the bandwidth nightmare: Cdn 10-12, 2022, pages 1061–1075. USENIX Association, compression format conversion attacks. In 2024 2nd 2022. International Conference on Big Data and Privacy Com- puting (BDPC), pages 97–106, 2024. [29] Bahruz Jabiyev, Steven Sprecher, Kaan Onarlioglu, and Engin Kirda. T-reqs: Http request smuggling with differ- [39] Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paran- ential fuzzing. In Proceedings of the 2021 ACM SIGSAC jape, Michele Bevilacqua, Fabio Petroni, and Percy Conference on Computer and Communications Security, Liang. Lost in the middle: How language models use CCS ’21, page 1805–1820, New York, NY, USA, 2021. long contexts. Transactions of the association for com- Association for Computing Machinery. putational linguistics, 12:157–173, 2024. [30] Ben Kallus, Prashant Anantharaman, Michael E. Lo- [40] Yuwei Liu, Junquan Deng, Xiangkun Jia, Yanhao Wang, casto, and Sean W. Smith. The HTTP garden: Discov- Minghua Wang, Lin Huang, Tao Wei, and Purui Su. ering parsing vulnerabilities in HTTP/1.1 implementa- Promefuzz: A knowledge-driven approach to fuzzing tions by differential fuzzing of request streams. CoRR, harness generation with large language models. In abs/2405.17737, 2024. Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security, CCS ’25, page [31] James Kettle. HTTP Desync At- 1559–1573, New York, NY, USA, 2025. Association for tacks: Request Smuggling Reborn, 2019. Computing Machinery. https://portswigger.net/research/ http-desync-attacks-request-smuggling-reborn. [41] Ruijie Meng, Martin Mirchev, Marcel Böhme, and Ab- hik Roychoudhury. Large language model guided proto- [32] James Kettle. HTTP/2: The Sequel is Al- col fuzzing. In Proceedings of the 31st Annual Network ways Worse, 2021. https://portswigger.net/ and Distributed System Security Symposium (NDSS), research/http2. 2024. [33] James Kettle. HTTP/1.1 must die: the desync endgame, [42] Seyed Ali Mirheidari, Sajjad Arshad, Kaan Onarlioglu, 2025. https://portswigger.net/research/ Bruno Crispo, Engin Kirda, and William Robertson. http1-must-die. Cached and confused: Web cache deception in the wild. 4918 35th USENIX Security Symposium USENIX Association In 29th USENIX Security Symposium (USENIX Secu- on Hot Topics in Networks, HotNets ’23, page 41–47, rity 20), pages 665–682. USENIX Association, August New York, NY, USA, 2023. Association for Computing 2020. Machinery. [43] Seyed Ali Mirheidari, Matteo Golinelli, Kaan Onarli- [56] Kaiwen Shen, Jianyu Lu, Yaru Yang, Jianjun Chen, oglu, Engin Kirda, and Bruno Crispo. Web cache de- Mingming Zhang, Haixin Duan, Jia Zhang, and Xi- ception escalates! In 31st USENIX Security Symposium aofeng Zheng. Hdiff: A semi-automatic framework for (USENIX Security 22), pages 179–196, Boston, MA, Au- discovering semantic gap attack in http implementations. gust 2022. USENIX Association. In 2022 52nd Annual IEEE/IFIP International Con- ference on Dependable Systems and Networks (DSN), [44] Hoai Viet Nguyen, Luigi Lo Iacono, and Hannes Feder- pages 1–13, 2022. rath. Your cache has fallen: Cache-poisoned denial-of- service attack. In Proceedings of the 2019 ACM SIGSAC [57] Martin Thomson and Cory Benfield. HTTP/2. RFC Conference on Computer and Communications Security, 9113, June 2022. CCS ’19, page 1915–1936, New York, NY, USA, 2019. Association for Computing Machinery. [58] Yuan Tian and Tianyi Zhang. Selective prompt anchor- ing for code generation. In Proceedings of the 42nd [45] OpenAI. gpt-oss, 2025. https://ollama.com/ International Conference on Machine Learning (ICML), library/gpt-oss. 2025. [46] OWASP. Cache Poisoning, 2019. https://owasp. [59] w3tech. Usage statistics of HTTP/3 for web- org/www-community/attacks/Cache_Poisoning. sites, 2025. https://w3techs.com/technologies/ [47] OWASP. Denial of Service, 2019. https: details/ce-http3. //owasp.org/www-community/attacks/Denial_ [60] Qi Wang, Jianjun Chen, Zheyu Jiang, Run Guo, Ximeng of_Service. Liu, Chao Zhang, and Haixin Duan. Break the wall from [48] Lorenzo Pisu, Federico Loi, Davide Maiorca, and Gior- bottom: Automated discovery of protocol-level evasion gio Giacinto. HTTP/3 will not Save you from Request vulnerabilities in web application firewalls. In 2024 Smuggling: A Methodology to Detect HTTP/3 Header IEEE Symposium on Security and Privacy (SP), pages (mis)Validations . In 2024 22nd International Sympo- 185–202, 2024. sium on Network Computing and Applications (NCA), [61] Watchfire. HTTP Request Smuggling, pages 97–104, Los Alamitos, CA, USA, October 2024. 2005. https://www.cgisecurity.com/lib/ IEEE Computer Society. HTTP-Request-Smuggling.pdf. [49] PSF. Python 3.12.7, 2024. https://www.python. [62] Yunze Wei, Kaiwen Chi, Shibo Du, Xiaohui Xie, Ziyu org/downloads/release/python-3127/. Geng, Yuwei Han, Zhen Li, Zhanyou Li, and Yong Cui. [50] Zhen Qin, Xiaodong Han, Weixuan Sun, Dongxu Large language model driven automated network pro- Li, Lingpeng Kong, Nick Barnes, and Yiran Zhong. tocol testing. In Proceedings of the 2025 Applied Net- The devil in linear transformer. arXiv preprint working Research Workshop, ANRW ’25, page 32–38, arXiv:2210.10340, 2022. New York, NY, USA, 2025. Association for Computing Machinery. [51] Julian Reschke. HTTP Authentication-Info and Proxy- Authentication-Info Response Header Fields. RFC 7615, [63] Linkai Zheng, Xiang Li, Chuhan Wang, Run Guo, September 2015. Haixin Duan, Jianjun Chen, Chao Zhang, and Kaiwen Shen. Reqsminer: Automated discovery of CDN for- [52] Julian Reschke. Hypertext Transfer Protocol (HTTP) warding request inconsistencies and dos attacks with Client-Initiated Content-Encoding. RFC 7694, Novem- grammar-based fuzzing. In 31st Annual Network and ber 2015. Distributed System Security Symposium, NDSS 2024, San Diego, California, USA, February 26 - March 1, [53] Julian Reschke. The Hypertext Transfer Protocol Status 2024. The Internet Society, 2024. Code 308 (Permanent Redirect). RFC 7538, April 2015. [54] Eric Rescorla. HTTP Over TLS. RFC 2818, May 2000. [64] Mingwei Zheng, Danning Xie, and Xiangyu Zhang. Large language models for validating network protocol [55] Prakhar Sharma and Vinod Yegneswaran. Prosper: Ex- parsers. In 2025 IEEE Security and Privacy Workshops tracting protocol specifications using large language (SPW), pages 56–64. IEEE, 2025. models. In Proceedings of the 22nd ACM Workshop USENIX Association 35th USENIX Security Symposium 4919 Table 9: The Amplification of RangeAmp on Every CDN. Test Type Metric Aliyun CDN Aliyun ESA Baidu Cloudflare Cloudfront Fastly Huawei Tencent CDN Tencent EdgeOne Client Response Code 206 206 206 206 206 206 206 206 206 Single_Range / Client Response Data (B) 1 1 1 1 1 1 1 1 1 If-Range Origin Response Data (B) 524288 400717.6* 542713* 29552200 1048576 2513443 524288 1 4097 Client Response Code 206 206 206 206 206 200 200 206 206 Multiple_Range Client Response Data (B) 1 1 1505 1473 1689 2513443 29552200 1681 1681 Origin Response Data (B) 524288 468256.2* 609378* 29552200 1048576 2513443 29552200 28673 12289 Client Response Code 416 416 416 416 200 200 416 416 416 Wrong_Range Client Response Data (B) 0 0 206 0 29552200 2513443 194 0 0 Origin Response Data (B) 327368* 417596* 373503* 29552200 29552200 2513443 524288 0 0 Client Response Code 412 412 412 200 412 412 412 412 412 If-Match / Client Response Data (B) 182 182 182 29552200 182 182 170 182 182 If-Unmodified-Since Origin Response Data (B) 182 182 490767* 29552200 182 182 524288 182 182 Client Response Code 304 304 304 304 304 304 304 200 200 If-None-Match / Client Response Data (B) 0 0 0 0 0 0 0 29552200 29552200 If-Modified-Since Origin Response Data (B) 29552200 29552200 633937* 29552200 179957.6* 2513443 524288 29552200 29552200 Table 10: Overview of attack vectors which present in HTTP/2 and HTTP/3 Alibaba Alibaba Tencent Tencent Attack Vector HTTP Version Baidu Cloudflare Cloudfront Fastly Huawei CDN ESA CDN EdgeOne HTTP/3 ✓ ✓ ✓ ✓ ✓ ✓ ✓ RangeAmp HTTP/2 ✓ ✓ ✓ ✓ ✓ HTTP/3 ✓ ✓ ✓ ✓ ✓ ✓ ✓ CondAmp HTTP/2 ✓ ✓ ✓ ✓ ✓ ✓ HTTP/3 ✓ ✓ ✓ ✓ ✓ ✓ ✓ HTTP Slow Attack HTTP/2 ✓ ✓ ✓ URI-based HTTP/3 ✓ ✓ CP HTTP/2 ✓ Multi-Host-Based HTTP/3 ✓ ✓ CP HTTP/2 ✓ ✓ ✓ ✓ ✓ ✓ HTTP/3 ✓ Foo-CRLF Attack HTTP/2 HTTP/3 ✓ ✓ ✓ ✓ ✓ Meta Characters HTTP/2 A Amplification Statistics ity when handling Single_Range and requests with If- Range. Similar patterns also appear for If-Match versus We measured the actual impact, i.e. the approximate band- If-Unmodified-Since, and for If-None-Match versus If- width amplification factor, of the two attacks reported in Sec- Modified-Since. Therefore, we merge these into three groups tions 5.1.1 and 5.1.2. To capture representative behavior, we in our results. repeated each packet transmission five times for every CDN. We then calculated the average length of the DATA frames received by the client and the average volume of data sent by B HTTP/2 vs HTTP/3 the origin server. Using the experimental setup described in We present the occurrence of conversion anomalies in Section 4.1, we requested a 29,552,200-byte PNG image from HTTP/3-to-HTTP/1.1 and HTTP/2-to-HTTP/1.1 across the origin server. The final statistics are shown in Table 9. We among CDNs in Table 10. observed that the response behaviors of both the CDNs and the origin server were generally stable. However, some CDNs prematurely closed the connection to the origin, preventing full data transmission. Consequently, the amount of data sent varied across attempts. We marked these unstable instances with an asterisk (*) in the table. Specifically, the Range-related fields are configured as follows: Single_Range uses range: bytes=0-0; Multiple_Range uses range: bytes=0-0, 1-1, 7-15, 428-1035, 1050-1258, 4095-4096, 524288-524290; and Wrong_Range uses range: bytes=100-90. The settings for the five conditional fields are shown in Figure 4. We observe that CDN behavior exhibits high similar- 4920 35th USENIX Security Symposium USENIX Association