--- type: Whitepaper title: "T-Reqs: HTTP Request Smuggling with Differential Fuzzing" description: A grammar-based differential fuzzer generates HTTP requests and mutates the request line, headers and body, then compares how ten servers, proxies and CDNs parse each one to find pairs that disagree on where a message ends. Those disagreements let an attacker smuggle a hidden second request past the front server, enabling cache poisoning, request hijacking and control bypass. resource: "https://bahruz.me/papers/ccs2021treqs.pdf" tags: [whitepaper, webseclist-reference, request-smuggling, desync, parser-differential, fuzzing, http, proxy, cdn, cache-poisoning, tooling, measurement-study] generated: by: webseclist-refs/1 at: "2026-08-11T17:38:02+00:00" status: deprecated stale_after: 2027-08-11 sources: - id: original resource: "https://bahruz.me/papers/ccs2021treqs.pdf" title: "T-Reqs: HTTP Request Smuggling with Differential Fuzzing" author: Bahruz Jabiyev, Steven Sprecher, Kaan Onarlioglu, Engin Kirda - id: capture resource: "https://web.archive.org/web/20220525035027/https://bahruz.me/papers/ccs2021treqs.pdf" also_at: [] authors: - Bahruz Jabiyev - Steven Sprecher - Kaan Onarlioglu - Engin Kirda canonical_url: "" cited_by: - "2021.md:24" commit: "" content_sha256: c161d6036a995d8daba4c0cf7131f27f3e949b877be799e67cb72ce93022e8eb depth: full depth_reason: default kind: whitepaper language: "" licence: unknown original_url: "https://bahruz.me/papers/ccs2021treqs.pdf" published: "" publisher: "" publisher_english: "" raw_sha256: 093372154d479b74abe1046f447e8db0c1fcd2861f0c5bf56da352e0b80ca745 retrieved_from: "https://bahruz.me/papers/ccs2021treqs.pdf" retrieved_kind: stored retrieved_utc: "2026-08-11T17:38:02+00:00" slug: t-reqs-http-request-smuggling-differential-fuzzing snapshot: 20220525035027 title_english: "" translation_file: "" translation_of: "" --- # T-Reqs: HTTP Request Smuggling with Differential Fuzzing **T-Reqs: HTTP Request Smuggling with Differential Fuzzing** - Bahruz Jabiyev, Steven Sprecher, Kaan Onarlioglu, Engin Kirda, Publisher not stated. - Published: date not stated - Original: - Preserved from: https://bahruz.me/papers/ccs2021treqs.pdf (stored) on 2026-08-11 - Capture timestamp: 20220525035027 - Licence: unknown Rights remain with the original author and publisher. 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T-Reqs: HTTP Request Smuggling with Differential Fuzzing Bahruz Jabiyev Steven Sprecher Northeastern University Northeastern University Boston, MA, USA Boston, MA, USA Kaan Onarlioglu Engin Kirda Akamai Technologies Northeastern University Cambridge, MA, USA Boston, MA, USA ABSTRACT semantics of these different servers to confuse them about message HTTP Request Smuggling (HRS) is an attack that exploits the HTTP boundaries, and consequently smuggles unintended requests into processing discrepancies between two servers deployed in a proxy- the connection inside the request body. origin configuration, allowing attackers to smuggle hidden requests HRS was first documented by Linhart et al. in 2005 [24]. However, through the proxy. While this idea is not new, HRS is soaring in the technique took off only recently when researchers proposed popularity due to recently revealed novel exploitation techniques novel variants and demonstrated attacks on high-profile targets and real-life abuse scenarios. (e.g., [6, 15, 19, 23]). Ultimately, smuggling was shown to be a serious In this work, we step back from the highly-specific exploits hog- threat leading to response queue and cache poisoning, which can ging the spotlight, and present the first work that systematically then be exploited for myriad nefarious purposes such as personal explores HRS within a scientific framework. We design an experi- data leakage, credential theft, session hijacking, denial of service, ment infrastructure powered by a novel grammar-based differential and security control bypass attacks, resulting in thousands of dollars fuzzer, test 10 popular server/proxy/CDN technologies in combi- in bug bounties (e.g., [5, 16, 17]). nations, identify pairs that result in processing discrepancies, and While these same researchers also released tools (e.g., [7, 36]) discover exploits that lead to HRS. Our experiment reveals previ- that partially automate the detection of HRS, these are largely ously unknown ways to manipulate HTTP requests for exploitation, intended for assisting website owners and penetration testers in and for the first time documents the server pairs prone to HRS. probing specific targets for vulnerabilities. These tools are also narrowly scoped, primarily testing for exploits that involve the CCS CONCEPTS manipulation of two particular HTTP headers, Content-Length and Transfer-Encoding, which govern how servers determine • Security and privacy → Web application security. HTTP message bounds. To date, HRS has not been studied in a systematic manner; the KEYWORDS disclosed vulnerabilities were instead driven by case studies target- HTTP Request Smuggling; HTTP Desync Attacks ing popular websites. In particular, previous work on HRS leaves ACM Reference Format: two important gaps in our understanding of HRS attacks. Bahruz Jabiyev, Steven Sprecher, Kaan Onarlioglu, and Engin Kirda. 2021. T- First and foremost, HRS is a system interaction problem, in- Reqs: HTTP Request Smuggling with Differential Fuzzing. In Proceedings of volving at least two HTTP processors on the traffic path. These the 2021 ACM SIGSAC Conference on Computer and Communications Security processors may not necessarily be individually buggy; but when (CCS ’21), November 15–19, 2021, Virtual Event, Republic of Korea. ACM, New used together, they disagree on the parsing or semantics of a given York, NY, USA, 16 pages. https://doi.org/10.1145/3460120.3485384 HTTP request, which leads to a vulnerability. This key aspect of HRS has not been explored in previous work. Next, previous attacks 1 INTRODUCTION focus on malicious manipulation of the two aforementioned HTTP Due to the continuing proliferation of web caches, proxies, cloud ser- headers. Whether the remaining HTTP headers, or the rest of an vices, and Content Delivery Networks (CDNs) that deploy massively- HTTP request, could be tampered with to induce similar processing distributed networks made up of these technologies, a typical HTTP discrepancies remains uncharted territory. request is often processed by multiple intermediate servers before In this paper, we present the first study that investigates HRS in it reaches its destination. HTTP Request Smuggling (HRS) is an a scientific framework, and we tackle the above research questions. attack that exploits the discrepancies between HTTP processing Namely, we present a novel experiment setup with 10 popular web servers and proxies: Apache, NGINX, Tomcat, Apache Traffic Permission to make digital or hard copies of all or part of this work for personal or Server (ATS), HAProxy, Squid, Varnish, Akamai, Cloudflare, and classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation CloudFront. We study these technologies in pairs, investigating on the first page. Copyrights for components of this work owned by others than ACM which combinations are vulnerable to HRS. To that end, we propose must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, a grammar-based fuzzer called T-Reqs that incorporates string to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. and tree mutations targeting a large variety of HTTP headers, the CCS ’21, November 15–19, 2021, Virtual Event, Republic of Korea request line, and the request body. T-Reqs employs a differential © 2021 Association for Computing Machinery. fuzzing strategy, first testing each target technology in isolation, ACM ISBN 978-1-4503-8454-4/21/11. . . $15.00 https://doi.org/10.1145/3460120.3485384 1 and then comparing responses to identify the pairs that behave Table 1: Breakdown of the chunked body. differently, signaling a potential vulnerability. 6\r\n Chunk size query=\r\n Chunk data Once we identify the combinations that exhibit discrepancies a;foo=bar\r\n Chunk size & chunk extension and the payloads that trigger them, we deploy every server pair funny+cats\r\n Chunk data in a proxy-to-origin formation for further experimentation and 0\r\n Last chunk X-Header:value\r\n Trailer part verification of our findings. We examine the conditions which cause \r\n Terminating CRLF the potential HRS attacks to succeed or fail in this setup, and finally demonstrate a range of exploits we discover. Our results show that attacks can indeed be induced by manipu- Chunked transfer encoding is an alternative encoding scheme lating every part of a request, and highlights that HRS is a complex available in HTTP/1.1, where the message body is split into multiple system interaction problem that can crop up as a result of seem- chunks transferred independently. This mechanism is useful for ingly innocuous processing discrepancies between pairs of web streaming applications, when the size of the data to be transferred technologies that are otherwise shown to be secure in isolation. is not known a priori [10]. We summarize our contributions as follows: Listing 2 shows the same request as before, this time using chun- • We present the most comprehensive study of HRS to date, ked encoding. Every data chunk is preceded by its size, specified and examine attacks within a scientific framework for the in hexadecimal. Both the size and the data are terminated by CRLF. first time in literature. Optionally, a chunk extension may immediately follow the size and • We propose a novel approach and experiment setup that contain metadata (e.g., a hash of the chunk data). The last chunk is identify the HTTP processing discrepancies between 10 pop- a regular but empty chunk with a size of zero. Again, optionally, the ular web servers and proxy services often used together. last chunk can include a trailer which is treated similar to message • We develop a grammar-based differential HTTP fuzzer called headers, used for sending additional information to the receiver. T-Reqs, and make it open source. Table 1 presents a breakdown of these chunk components. • We discover novel HRS payloads made possible by manipu- Note that the Transfer-Encoding: chunked header in Listing 2 lating HTTP request parts beyond the Content-Length and indicates to the receiver that chunked encoding is in effect. When Transfer-Encoding headers. using chunked encoding, sending the Content-Length header is • We systematically examine the practical conditions that de- not meaningful, and in fact, according to RFC 7230, this is prohibited: termine the success of HRS. “A sender MUST NOT send a Content-Length header field in any Availability. T-Reqs is open-source and publicly available on message that contains a Transfer-Encoding header field.” [10] the authors’ websites. Ethical Considerations. This study was conducted within a 2.2 HTTP Request Smuggling (HRS) controlled experiment setup, and no attacks were launched against HRS stems from a discrepancy between the HTTP processing be- any external entities. We followed the established coordinated- haviors of two servers that process the same request on the traffic disclosure best practices; we notified all tested technology vendors path. These servers could be any technology that intercepts, parses, of our findings, provided them with a copy of this paper, and made interprets, or forwards the request, including CDNs, stand-alone our data and team available for further assistance. proxies, web caches, load balancers, or security products. In this text, we call the first server receiving the request the entrypoint, 2 BACKGROUND & RELATED WORK and the next one the exitpoint. While this abstraction is sufficient In this section we explain the basic terminology we use in the rest for our discussion, note that a typical request may be processed by of this text, and summarize how HRS attacks work. more than two such entities, and a hazardous combination of any two could lead to attacks. 2.1 HTTP Requests & Chunked Encoding HRS involves a maliciously-crafted request such that the entry and exitpoints disagree on the bounds of the message. All docu- Listing 1 illustrates the structure of a typical HTTP request, made mented attacks we previously discussed in Section 1 achieve this up of the following three components. by including both the Content-Length and Transfer-Encoding: (1) Request Line. Line 1 is the request line for this request, which specifies the HTTP method (POST), the requested URI (/search), and the protocol version (HTTP/1.1). 1 POST /search HTTP/1.1 1 POST /search HTTP/1.1 (2) Header Block. This section follows the request line, listing 2 Host: example.com 2 Host: example.com 3 Content-Length: 16 3 Transfer-Encoding: chunked header fields and values that define various parameters of the com- munication. On lines 2-3, the Host header specifies the endpoint 4 4 query=funny+cats 6 the request should be dispatched to, and Content-Length indicates 5 5 6 6 query= the length of the message body. 7 7 a;foo=bar (3) Request Body. Separated from the header block by a blank 8 8 funny+cats line containing a carriage return and a line feed (often indicated by 9 9 0 CRLF or \r\n), the request body starts on line 5 and contains the 10 . 10 X-Header:value message payload. Here, the body consists of a parameter and its value, query=funny+cats. Listing 1: Regular body. Listing 2: Chunked body. 2 1 POST /search HTTP/1.1 ::= 2 Host: example.com ::= 3 Content-Length: 33 ::= "POST /search HTTP/1.1\r\n" | "PUT / HTTP/1.1\r\n" 4 Transfer-Encoding: ; chunked ::= | 5 ::= "Host: example.com\r\n" 6 0 ::= "Content-Length: 16\r\n" 7 ` ::= "\r\n" 8 GET /img/i.jpg HTTP/1.1 ::= "query=funny+cats" | "query=carrots" 9 X:X Listing 6: Example CFG for a simple HTTP request. Listing 3: Malicious smuggler request. until the cache expires. This is but one example, and researchers 1 GET /js/j.js HTTP/1.1 1 GET /img/i.jpg HTTP/1.1 have shown that HRS can be utilized for general classes of attacks 2 Host: example.com 2 X:X GET /js/j.js HTTP/1.1 such as cache poisoning, cache deception, session hijacking, circum- 3 A 3 Host: example.com vention of security controls, and response queue poisoning, as well 4 A 4 as abusing application specific design flaws [6, 15, 19, 23, 24, 35]. 5 A 5 . Listing 4: Incoming request. Listing 5: Smuggled request. 2.3 Differential Fuzzing Fuzzing is a well-established software testing approach with many applications in systems security [14, 38]. Of particular interest for chunked headers in the request – if the entrypoint honors one our purposes is differential fuzzing, based on the idea of differen- header and the exitpoint the other, they parse the message body tial testing [27], where the focus is to identify differing behavior differently, and an HRS attack is possible. Even when both servers between applications when given the same input. To name recent are strictly RFC compliant so that they reject or normalize messages examples, this method was used to detect side-channel attacks [32], containing both headers, and attacker can still abuse header parsing to expose vulnerabilities in parsers and applications [33], and to discrepancies (e.g., by introducing invisible characters or minor find RFC violations in TLS libraries [37]. syntax errors into headers) and prevent one of the servers from To apply this technique to the HTTP protocol, we construct our recognizing an invalid header combination. fuzzer using a custom context-free grammar (CFG). Context-free Let’s illustrate the attack through an example. grammars are sets of rules that allow for a formal definition of a (1) The attacker crafts a smuggler request which includes a sec- structure, e.g., an HTTP request, and values that correspond to ond hidden request inside the message body. Listing 3 shows such a that structure. From this grammar, we are able to generate valid request including Content-Length: 33 and Transfer-Encoding: inputs to our system, and make our fuzzing mutations based off of ;chunked together. Note the extra semicolon in the latter, which them. An example CFG that produces an HTTP request is shown in will serve to confuse the message parser in the next step. Listing 6. Grammar-based fuzzers have also been previously used (2) The entrypoint receives the request, but cannot correctly for software bug hunting (e.g., [1]). parse Transfer-Encoding: ;chunked due to the semicolon. The A CFG has four components: a start symbol, non-terminal sym- server disregards chunked encoding and instead parses the message bols, terminal symbols, and production rules. The start symbol is body according to the Content-Length: 33 header. As a result, where the expansion of a CFG starts from. In Listing 6, the start the entrypoint forwards all 33 bytes shown between lines 5-9 to symbol is denoted by . Symbols surrounded by <> are non- the next hop. terminals, meaning they are expanded before the input is fully (3) The exitpoint receives the same request, correctly parses generated. For example, is expanded to a sequence of Transfer-Encoding: ;chunked thanks to its lenient parser ig- other non-terminal symbols, whereas, can be expanded noring errors, and processes the body in chunks. Consequently, into multiple terminal strings. Finally, production rules define how the exitpoint treats lines 6-7 as the terminating empty chunk, and symbols are expanded. Each line in Listing 6 is a production rule. ignores lines 8-9. When this CFG is fully expanded, one of the possible results is the (4) The unprocessed data shown on lines 8-9 remain in the re- request shown in Listing 1. quest buffer of the exitpoint. When eventually another request arrives through the same connection (Listing 4), it is appended to 2.4 Other Related Work this unprocessed data, making up a brand new request (Listing 5). An emerging line of research is the application of HRS to higher This new request is only seen and processed by the exitpoint; the HTTP protocol versions; in particular, Emil Lerner and James Kettle attacker has successfully smuggled it through the entrypoint. independently presented attacks on HTTP/2 [18, 22]. These utilize In this example, assuming that the entrypoint is a web cache the same techniques as before, but exploit flaws in the protocol and the exitpoint a web application server, the attacker uses HRS downgrade mechanisms when an entrypoint converts HTTP/2 to to launch a cache poisoning-based denial-of-service attack. Specifi- HTTP/1.1 before forwarding requests to the exitpoint. Our work cally, the web cache expects a JavaScript file in response (see List- does not explore this area. ing 4), but instead receives an image from the application (see List- Beyond the presentations, proof-of-concept exploits, and white ing 5) and erroneously caches that, likely breaking the application papers we discussed so far, there is no academic literature on HRS as 3 generating mutating Input requests requests processing behavior of each individual server, and combine the Mutated Grammar Inputs Inputs results to identify servers that process the same request differently. At this stage, we experiment with each server in isolation to analyze their individual behavior. Each server runs in a reverse- sending requests proxy mode, where they receive requests and forward them on forwarding to our feedback server. The feedback server gleans information storing parsed about the processing behavior of the tested server by analyzing requests Feedback requests HTTP Feedback Server Server the forwarded request. This information is stored in a database for Database later analysis. We specifically look for mismatches between parsed message body lengths, and label those as discrepancies. Figure 1: Inputs are generated from a grammar, mutated, and sent Figure 1 depicts this whole process, where the top row is internal to the tested server. The feedback server collects feedback from the requests forwarded by the tested server and stores it for analysis. to T-Reqs, and the bottom row is the rest of the experiment infras- tructure. Note that, in order to avoid adding a confounding layer of parsing in our own tools, we use low-level network programming. of this writing. However, while this paper is the first work exploring Stage 2. We then reduce our set of discrepancies found in Stage HRS within a scientific framework, there exists studies that propose 1 based on rules and heuristics detailed in Section 5. Essentially, other ways to abuse HTTP processing discrepancies. we associate mutation sets with the server pairs they cause a dis- Omer Gil presented a novel cache poisoning attack called Web crepancy for, minimize them down to a representative group, and Cache Deception (WCD), which exploits an object cacheability dis- finally, manually classify these groups based on their mutation pat- agreement between a web server and a cache, resulting in data terns. We stress that this manual classification is not mandatory; leaks in public caches [12, 13]. Mirheidari et al. generalized WCD we merely include this step to simplify the presentation for our as a path confusion problem caused by a discrepancy in the in- readers by attaching intuitive labels to similar discrepancy types. terpretation of a requested URL [28], and conducted a large-scale Stage 3. Finally, we verify the exploitability of the results from measurement to identify vulnerable sites in the wild [29]. Stage 2. To achieve this, we layer and deploy suspected vulnerable Nguyen et al. presented a different take on cache poisoning, HTTP server pairs behind each other. We use a testing method crafting HTTP requests that are considered valid by a web cache inspired by prior work to check whether a given mutated request while triggering an error at the origin server [31]. As a result, can really be used for HRS. We present the details of this method the error response is erroneously cached, resulting in a denial-of- in Section 5.3. service attack. Similarly, Chen et al. exploited HTTP servers that (Q2) What parts of a request can induce processing dis- respond differently to ambiguities in the Host header values, which crepancies? Previous work has explored the parsing discrepan- once again leads to cache poisoning [4]. cies involving Content-Length and Transfer-Encoding headers. Whether the remaining request components can be abused to simi- 3 RESEARCH QUESTIONS & METHOD lar effect remains an open question. We address (Q2) by considerably expanding that scope. Not only Previous work on HRS presents valuable concepts behind the at- do we allow T-Reqs to mutate additional headers, but we also inves- tack, but does not explore the issue in depth or breadth, instead tigate whether abusing the request line and the message body can demonstrating impact through specific case studies. Our work is also induce discrepancies, opening up novel attack vectors. We run motivated by this knowledge gap. Below, we detail our guiding three separate experiments, one for each HTTP request component research questions, and explain our methods to answer them. listed above, each following the same stages we designed for ad- (Q1) Can we systematically test for HRS at scale? dressing (Q1). In each experiment, we only allow T-Reqs to mutate (Q2) What parts of a request can induce processing discrepancies? the part under focus, while keeping the other two request com- (Q3) What escalates a processing discrepancy to HRS? ponents unmutated. This makes it feasible to pinpoint and reason (Q4) What technology stacks are at risk? about the exploitable discrepancies in isolation. (Q1) Can we systematically test for HRS at scale? Previous (Q3) What escalates a processing discrepancy to HRS? The work relies on a combination of manual testing and basic tools presence of a discrepancy is a red flag, but not all discrepancies designed to target specific controlled environments (e.g., [7, 36]) for necessarily lead to HRS. In particular, while exploits involving attack discovery. In contrast, we aim to design a fully-automated, Content-Length and Transfer-Encoding headers are intuitive generalizable, and extendable methodology that can explore HRS (i.e., they directly affect the body parsing behavior, which is a pre- at scale and discover previously unknown venues for exploitation. requisite for HRS), why the discrepancies in other request compo- To address (Q1), we design a multi-stage experiment powered by nents may lead to an attack is not obvious. a novel CFG-based differential fuzzer, T-Reqs. This is an automated To explore (Q3), while we verify our findings in Stage 3 of the process, eliminating the manual labor and narrow scope hindering experiment, we analyze the conditions affecting exploitability. We previous work. This methodology and infrastructure to explore document the novel and successful exploit mechanisms we identify, HRS systematically equips us to answer the remaining research and also the failures that hinder attacks in practice. questions. Below, we briefly describe the 3 stages of our experiment. (Q4) What technology stacks are at risk? HRS is a system Stage 1. We first point T-Reqs to a set of popular HTTP servers interaction problem involving two HTTP processors, which may for testing, and send identical requests to each. We record the not be flawed when operating in isolation; but stacked together they 4 lead to a vulnerability. Previous work on HRS has made no attempt 1 PO R T / /search HTTP/1. 1 1 HTTP /search /search HTTP /1.1 to measure what server combinations are prone to smuggling. 2 Host: example.com 2 Host: example.com While we cannot feasibly test all technology combinations in 3 Content-Length: 13 3 Content-Length: 13 existence, we make the first systematic attempt to answer (Q4) 4 4 by designing an experiment that measures and documents the 5 query=bananas 5 query=bananas hazardous interactions between 10 HTTP processors. Listing 7: String mutations. Listing 8: Tree mutations. Specifically, we pick popular web server, proxy, and CDN tech- nologies in use today that make up a large portion of the Internet: Apache, NGINX, Tomcat, Apache Traffic Server (ATS), HAProxy, 4.3 String Mutations Squid, Varnish, Akamai, Cloudflare, and CloudFront. For specific If a symbol is string mutable, then a random character can be versions, please see Appendix A. To test CDN vendors, we subscribe deleted, replaced, or inserted at a random position inside that sym- to their free or trial tier services. We configure each technology to bol. To add or replace characters, an external character pool can be run as a reverse-proxy fronting our feedback server (except Tomcat, defined. T-Reqs uses the ASCII character set (codes 0-127) as the which has no reverse-proxy mode, so we run a Java servlet on it that character pool suitable for HTTP requests. echoes back the received requests). We use default configurations, Listing 7 shows an example. The last character in the protocol save for turning off buffering in NGINX to speed up testing, and version (1) is deleted, a letter in the method name (S) is replaced disabling caching for clean experiment runs. with R, and a forward slash is inserted at the start of the URI. 4 T-REQS SYSTEM DESIGN 4.4 Tree mutations We now detail the design of T-Reqs, our grammar-based differential If a symbol is tree mutable, then a random symbol can be deleted, HTTP fuzzer. T-Reqs is capable of generating HTTP requests as replaced, or inserted at a random position. To add or replace sym- inputs from a grammar, manipulating them with string and tree bols, an external pool of elements can be defined. T-Reqs uses the mutations, and sending them to multiple HTTP servers in parallel list of all symbols marked mutable as the external symbol pool. for testing. For example, the request line is represented by and has several sub-elements including , , , 4.1 Input Generation and . In Listing 8, it is assumed that is tree mutable, and the following tree mutations are applied: 1) To ensure that we test all relevant components of an HTTP request, is replaced by , 2) an extra is inserted after and their applicable values, T-Reqs uses a context-free grammar the current URI, and 3) the existing is deleted. (CFG) to generate inputs. Each generated input is a valid HTTP request constructed by following one of the paths provided by the CFG, chosen randomly to ensure uniform testing. We record each 5 EXPERIMENT DETAILS AND RESULTS random seed as the input ID to aid in reproducibility. In this section, we provide details and discuss results from the When building our input from the included grammar, we adopt experiments listed in Section 3. We first run three separate experi- a tree structure. The start symbol becomes the root, and each non- ments on each part of the HTTP request utilizing T-Reqs to expose terminal is a non-terminated node in the tree. The leaves of the discrepancies in message body parsing behavior. Next, we reduce, tree, once fully expanded, are made up of the terminal symbols (i.e., minimize, and categorize the sets of mutations that cause these dis- string literals), and when combined form our HTTP request. We crepancies to understand what leads servers to disagree on message present the specific CFGs used for our experiments in Section 5. boundaries. We then verify the HRS potential of these categories, and explore reasons why they succeed or fail. 4.2 Mutating Inputs 5.1 Stage 1 - Finding Discrepancies In order to exercise the parsers of, and consequently trigger process- For this stage, we run three separate experiments on each part of the ing discrepancies between, different HTTP servers, T-Reqs makes HTTP request: the request line, request headers, and request body. mutations on the valid requests generated in the previous step. Table 2 shows the duration of each experiment, and the number Symbols, each corresponding to an HTTP element, can be marked of requests generated and tested. We found that mutations in the in one of two ways: string mutable or tree mutable. If a symbol request line experiment caused more errors (e.g., 400 Bad Request), is not marked, it is assumed to be immutable. While string mu- even when bounding the total number of mutations to two. We see tations (e.g., character insertion, deletion) make small changes to more mutations an hour in the request line experiment because parts of an input, tree mutations lead to structural changes (e.g., re- these errors are significantly faster for HTTP servers to handle peated method specification, missing protocol version). This allows than valid requests. T-Reqs to test both trivial and major changes to an input. Mutation To make T-Reqs more efficient, we supply different grammars operations are formally defined in Appendix B. and mutable symbols for each experiment as detailed below. In each iteration, T-Reqs randomly applies up to 2 mutations on each input. This upper bound makes the impact analysis of specific 5.1.1 Request Line Experiment Details. Listing 9 shows the gram- mutations feasible, as well as helping us avoid changing requests mar for the request line experiment. We test the standard HTTP to the degree that they are unrecognizable by the servers. methods as defined by their RFCs [9, 11]. Note that we do not 5 Table 2: General information about experiments. ::= Name Duration # Inputs ::= ::= Request line 70 hours 8,857K ↩→ Request headers 94 hours 3,096K ::= "GET" | "HEAD" | "POST" | "PUT" | "DELETE" | Request body 72 hours 2,051K ↩→ "CONNECT" | "OPTIONS" | "TRACE" | "PATCH" ::= " " ::= "/_URI_" Table 3: Mutability of request line symbols. ::= "HTTP" String Mutable Tree Mutable ::= "/" ::= "0.9" | "1.0" | "1.1" | "2.0" | "3.0" ::= "\r\n" ::= "Host: _HOST_\r\nConnection:close\r\nX-Request-ID: ↩→ _REQUEST_ID_\r\n" ::= "Content-Length: 5\r\n\r\nBBBBB" Table 4: Mutability of request body symbols. String Mutable Tree Mutable Listing 9: CFG for request line experiment. ::= ::= ↩→ ::= " /_URI_ " Table 5: Experiment success values. ::= "HTTP/0.9" | "HTTP/1.0" | "HTTP/1.1" ::= "GET" | "HEAD" | "POST" | "PUT" | "DELETE" | Experiment Name # Inputs # Successful ↩→ "CONNECT" | "OPTIONS" | "TRACE" Request line 8,857K 5K ::= "\r\nHost: _HOST_\r\nConnection:close\r\nX-Request-ID: Request headers 3,096K 1K ↩→ _REQUEST_ID_\r\n" Request body 2,051K 595K ::= | ↩→ | ↩→ | ↩→ test the HTTP/2 or HTTP/3 protocols, but our generated requests ::= | | (truncated) | merely appear to use them. As we show, the protocol values still ↩→ | ::= "\r\nA\r\nBBBBBBBBBB\r\n0\r\n\r\nBBBBB(truncated)" trigger unexpected parsing behaviors nonetheless. Table 3 details what symbols of the grammar are marked as string (truncated) or tree mutable. In order to test mutations solely on the request Listing 10: CFG for request headers experiment. line, we mark only those symbols as mutable. 5.1.2 Request Headers Experiment Details. Listing 10 details the ::= grammar for the request headers experiment. We used all standard ::= ↩→ HTTP request headers as detailed in the "Message Headers" reg- ::= "POST /_URI_ HTTP/1.1\r\nHost: istry of IANA [20], and valid values from their corresponding RFC ↩→ _HOST_\r\nConnection:close\r\nX-Request-ID: _REQUEST_ID_\r\n" documents. For a full listing of all 67 headers and values used in ::= | this experiment, refer to Section C in Appendix. ↩→ ::= "Content-Length: 200\r\n" Mutable symbols for this experiment consist of the 67 headers ↩→ ::= "Transfer-Encoding: chunked\r\n" and their sub-elements depicted in the full grammar definition. ::= "Trailer: Content-Length\r\n\r\n" | "Trailer: String literals in the grammar are marked as string mutable, while ↩→ Transfer-Encoding\r\n\r\n" | "Trailer: Foo\r\n\r\n" | "\r\n" ::= | all other symbols are marked as tree mutable. ↩→ ::= 5.1.3 Request Body Experiment Details. Listing 11 depicts the gram- ↩→ | mar for the request body experiment. This experiment focuses on ↩→ chunked bodies, as they have a complex structure with the highest ::= "4" ::= ";foo=bar" potential for parsing discrepancies. We include all chunked body ::= "BBBB" components in the input grammar, namely, the chunk size, chunk ex- ↩→ ::= | ↩→ tension, chunk data, trailer and last chunk. We also add the Trailer ↩→ ::= "0" header to the grammar, since it is required to include additional ::= "Transfer-Encoding: chunked\r\n" | fields at the end of chunked messages. Unlike other experiments, ↩→ "Transfer-Encoding: identity\r\n" | "Content-Length: 180\r\n" | ↩→ "Bar: Foo\r\n" we fix the method to POST and the version to 1.1. ::= "\r\n" The grammar defines a symbol called which adds ::= "DDDDDDDDDD(truncated)" 200 D characters after the last chunk. This symbol lets us deter- mine whether the experiment server used Transfer-Encoding Listing 11: CFG for the request body experiment. or Content-Length when parsing the body. If the server uses Content-Length, the output will include our padding values; oth- Table 4 shows that three chunked body symbols are marked as erwise, the padding will be omitted from the output, since this is tree mutable, while the other symbols are string mutable. The rest the expected behavior for chunked bodies. of the request remains immutable. 6 Table 6: Examples for each request line mutation category. Category Request Line Entrypoint-Exitpoint Apache-Akamai mangled Apache-ATS h EAD / HTTP/1.1\r\n method HAProxy-Akamai HAProxy-ATS distorted GET / H h TTP/1.1\r\n ATS-Squid protocol invalid ATS-Akamai GET / HTTP/ 1 .1 9 \r\n version ATS-Squid manipulated CONNECT / HTTP/1.0 Varnish-NGINX termination \r\n \r\n HAProxy-NGINX embedded OPTIONS / HTTP/ OPTIONS Akamai-Squid request lines / HTTP/0.9\r\n 1.1\r\n multiple GET / HT T P / /1.1\r\n HAProxy-Squid mutations various Apache-ATS method HAProxy-ATS TRACE / HTTP/ 1.0 \r\n version Squid-ATS combinations Varnish-ATS Figure 2: Request line mutation categories affecting server pairs. body of the request, while the exitpoint (Akamai or ATS) ignores it. The remaining servers return an error because of the mutation. For 5.2 Stage 2 - Discrepancy Reduction and brevity, in the remainder of this section an error should be assumed Classification if an experiment server is not mentioned explicitly. We now detail the process for determining mutation success, and Distorted Protocol. This category consists of mutation sets that present our classifications of mutation sets that cause discrepancies. replace one character in the protocol name with another, usually adding the character h to the beginning of the protocol, or changing Successful Mutation Sets. To determine if a mutation from the the case of an existing letter. Table 6 shows a mutation that causes previous stage has HRS potential, we first need to define what a a discrepancy between ATS and Squid. ATS handles the mutation successful mutation is. and parses the message body whereas Squid ignores the body. A successful mutation set causes a discrepancy in the body pars- Invalid Version. Mutation sets with this classification add a digit, ing behavior in at least one server pair, where the absence of the replace a digit with another, or remove a digit from the beginning mutation set does not. Essentially, if a mutation set causes a dis- and adds one to the end. This category of mutations primarily crepancy that the base unmutated request does not, we consider involve digits, keeping the versions numeric, yet invalid. that a successful mutation set. Manipulated Termination. These mutations primarily add a Table 5 shows the number of successful mutations for each ex- space, tab, or CRLF before the CRLF that terminates the request periment. To gain deeper insights into the causes and potential for line. The example in Table 6 triggers a discrepancy when Varnish HRS, we set out to reduce this set further. We reduce mutation sets or HAProxy is the entrypoint, and NGINX is the exitpoint. The based on the following definition. entrypoint determines that the body is what directly follows from A mutation set 𝑀2 is reducible to 𝑀1 iff. 𝑀1 ⊆ 𝑀2 and 𝑠 (𝑀2 ) ⊆ the double CRLF, whereas the exitpoint ignores the body completely. 𝑠 (𝑀1 ) where 𝑠 (𝑀) is the set of server pairs which disagree about Embedded Request Lines. This category includes mutation sets parsing on an input mutated by a mutation set 𝑀. that insert a whole request line into the existing request line at Classification of Mutation Sets. We classify mutation sets various positions, including after the method and the protocol. The based on their mutation pattern beyond the specifics of how the example in Table 6 has a request line inserted after the protocol. mutations are carried out. For example, all mutations deleting, re- Multiple Mutations. We classify mutation sets into this cate- placing, or inserting a character in the method of a request line gory when the individual mutations alone do not trigger a discrep- follow the same pattern: Distorting Method. We explore these cate- ancy, but together they do. These mutation sets mainly have two gories and the server pairs they affect for each experiment below. forms: 1) the method name is mutated while another CRLF is added next to the terminating CRLF, or 2) a character is deleted from the 5.2.1 Request Line Experiment. Figure 2 lists all mutation cate- protocol name and a second slash is added after the protocol name. gories affecting the request line, and the server pairs that disagree Various Method Version Combinations. Sometimes inconsis- on body parsing for each category. Table 6 shows examples for each tent behavior is triggered by bringing various methods and versions mutation category and the impacted server pairs. together with no need for mutations. In the example in Table 6, Mangled Method. This class comprises mutation sets which ATS ignores the message body for TRACE requests, whereas Apache, modify the method name. Mutations can change the case of a letter, HAProxy, Squid, and Varnish do not. replace the entire method name, or modify it in another way. The first row in a Table 6 shows an example where a single mutation 5.2.2 Request Headers Experiment. Figure 3 lists all server pairs causes a discrepancy between 4 different server pairs. We observe and mutation categories affecting request headers. Table 7 presents that the entrypoint (Apache or HAProxy) parses and forwards the examples as before; however, each example shows both the method 7 Table 7: Examples for each request header mutation category. Category Method ; Request Header Entrypoint-Exitpoint Tomcat-Akamai distorted GET;Transfer-Encoding: ATS-Akamai header value chunked , \r\n HAProxy-Akamai NGINX-ATS manipulated GET;Transfer-Encoding: Cloudflare-ATS termination chunked\r\n ␣ {Header}:{Value}\r\n CloudFront-ATS expect POST; Expect : NGINX-Apache header 100-continue \r\n (truncated) Cloudflare-Squid identity POST; Transfer-Encoding : CloudFront-Squid encoding identity \r\n (truncated) v1.0 chunked POST;Transfer-Encoding: Apache-Tomcat encoding chunked \r\n (truncated) POST;Transfer-Encoding: Cloudflare-Tomcat double identity\r\n CloudFront-Tomcat transfer- Transfer-Encoding: Cloudflare-Akamai encoding chunked \r\n (truncated) various method OPTIONS-0.9 ; HAProxy-Squid version Content-Length:5\r\n Akamai-Squid combinations Figure 3: Request header mutation categories affecting server pairs. Various Method Version Combinations. Similar to the request line experiment, various options for methods and versions defined name and the mutation, since successful mutations in this experi- in the input grammar are combined in different ways to generate ment vary from method to method. our input requests. In this experiment, these combinations are also Distorted Header Value. This category includes mutation sets combined with various headers including Transfer-Encoding, and that add specific characters, such as a vertical tab, new page, space, can cause discrepancies without a mutation. plus, and comma to the beginning and end of specific header values. The headers in this category are Transfer-Encoding: chunked 5.2.3 Request Body Experiment. Figure 4 lists the server pairs that and Content-Length: LENGTH. In Table 7, a comma is appended to have discrepancies with mutation categories affecting the request the header value. Akamai ignores the request body, while Tomcat, body, and Table 8 shows examples. HAProxy and ATS parse and forward it. Chunk-Size Chunk-Data Mismatch. These mutations add Manipulated Termination. Mutations in this category mainly to or remove a character from chunk data to make its size differ- insert a space or tab after the header-terminating CRLF, resulting ent from what is claimed in the chunk size. This causes Akamai in parsing discrepancies. In Table 7, a space is added after the CRLF to process the request body using Content-Length and ignore following the first header value. As a result, ATS ignores the request Transfer-Encoding, while every other server (except Tomcat and body, whereas NGINX, Cloudflare and CloudFront do not. Apache that give an error) continues to use Transfer-Encoding. Expect Header. We find that the Expect header is interpreted Manipulated Chunk-Size Termination. Mutations in this cate- differently by Apache. When Apache receives a request with this gory modify the CRLF terminating the chunk size, and typically add header and its 100-continue value, it ignores the body in the a character such as a new page, semicolon, or space. This causes Aka- request as opposed to every other server we experimented with. mai to make a different preference between Transfer-Encoding Identity Encoding. When a request has a Transfer-Encoding and Content-Length headers compared to the other servers. header with the identity value, Squid and ATS ignore the mes- Manipulated Chunk-Extension Termination. In this cate- sage body. Tomcat, Akamai, Cloudflare and CloudFront parse and gory, mutation sets remove a part of the newline which termi- forward the body. nates the chunk extension. Usually, the carriage return character V1.0 Chunked Encoding. Tomcat does not support chunked is deleted. Again, this causes Akamai to use the Content-Length encoding in HTTP version 1.0. Thus, this causes an inconsistency header instead of the Transfer-Encoding header. between Tomcat and all other servers we experimented with. When Manipulated Chunk-Data Termination. These mutations a request has both Transfer-Encoding and Content-Length head- remove the terminating CRLF, partially or wholly from the chunk ers, all servers prefer the former, whereas Tomcat prefers the latter. data part of the request. In the example shown in Table 8, the CRLF Double Transfer-Encoding. We observe an interesting behavior is completely removed at the end of the first chunk data. when a request has two Transfer-Encoding headers. When the first Mangled Last-Chunk. Mutations in this category include re- header has the value identity and the second chunked, Cloudflare moving one CRLF before the last chunk, inserting digits next to the and CloudFront use the Content-Length header, while Tomcat, chunk size in the last chunk (as seen in Table 8), or removing the HAProxy, and Akamai use Transfer-Encoding to dictate message entire last chunk itself. Unlike HAProxy, Squid, and CloudFront, body parsing. Akamai does not treat the request body as chunked-encoded. 8 Table 8: Examples for each request body mutation category. Category Request Body Entrypoint-Exitpoint chunk-size Akamai-NGINX 4\r\n B BBB chunk-data Akamai-Varnish \r\n0\r\n\r\n mismatch (truncated) manipulated Cloudflare-Akamai 4 \t \nBBBB chunk-size Squid-Akamai \r\n0\r\n\r\n termination (truncated) manipulated Akamai-Cloudflare 4;foo=bar \r \nBBBB chunk-extension Akamai-ATS \r\n0\r\n\r\n termination (truncated) manipulated 4\r\nBBBB \r\n 4 CloudFront-Varnish chunk-data \r\nBBBB Akamai-Varnish termination \r\n0\r\n\r\n (truncated) HAProxy-Akamai mangled 4\r\nBBBB Squid-Akamai last-chunk \r\n 2 0\r\n\r\n (truncated) the others failed. We note that for all but two specific cases, the pay- load format allowed with these HRS attack vectors is unrestricted. Figure 4: Request body mutation categories affecting server pairs. For the following, please reference Figure 5 for the server combi- nations and mutation categories that successfully carried an HRS payload, and Figure 6 for a breakdown of the server combinations 5.3 Stage 3+ - Determining Discrepancy HRS and reasons mutations failed for them. Potential Stage 2 yielded classified mutation sets that are reduced to represen- 5.3.1 Request Line Mutations. Among the request line mutations, tative examples, and the server pairs that have parsing discrepancies only two categories failed to carry out HRS, Distorted Protocol, on said sets. We now determine if these parsing discrepancies can and Multiple Mutations. We found that some servers normalized be used for HRS. For every unique server pair that appears in the re- parts of the request line before forwarding them to the next server sults from Stage 2, we set up our lab to position them as entrypoint when they encountered our mutations, or just flat out closed the and exitpoint on path. We ensure that the connection between the connection. We observed servers being particularly sensitive to two servers is persistent, as HRS requires this. invalid requests, which is very common in these two mutation To understand if our mutated requests lend themselves to HRS, categories. The successful categories contained less invalidating we craft a smuggler request as shown in Listing 12 for every muta- mutations, and thus proved more fruitful. tion (Listing 12 shows a Mangled Method mutation). Immediately In one case, there was a restriction on the format of the smug- after we send the smuggler request, we send a benign request like gled content. Using a specific method version combination with Listing 13 on the same connection. If our smuggler request was Squid-Akamai, the entrypoint expected the request to be in chun- successful, our payload (shown in Listing 14) will invalidate the ked encoding. Therefore, the smuggled content had to follow the valid request, and the exitpoint will return a 400 Bad Request chunked format. error message in a response to the benign request. Unlike others, Varnish cleans its connection, preventing HRS. To ensure the error message did not come from the entrypoint, When Varnish receives a GET request with a body, even though it we note that each server has their own unique fingerprint in the ignores the body, it does not leave the body in the connection. HTML error page returned, and verify the exitpoint’s fingerprint. 5.3.2 Header Mutations. We observe that the categories Manip- For each experiment, we now detail the categories of mutations ulated Termination, Expect Header, Identity Encoding, and Double that can be used for HRS, and discuss why they succeeded while Transfer-Encoding failed to work in any server combination. Similar to the request line mutations, mutations to the Transfer-Encoding 1 h EAD / HTTP/1.1 1 POST / HTTP/1.1 and Content-Length headers often were not preserved. Servers 2 Host: example.com 2 Host: example.com typically re-wrote their own headers in place of our mutations 3 Content-Length: 2 3 Content-Length:5 based off of what they parsed, effectively stopping all HRS. For the 4 4 Expect Header failure, that category only affected Apache, and 5 A␣ 5 AAAAA Apache closes the connection after receiving these requests. Similar to the request line experiment, only one case restricted Listing 12: Smuggler request. Listing 13: Benign request. the format of the smuggled content. When a request with a distorted 1 A␣ POST / HTTP/1.1 header value was sent to ATS-Akamai, ATS required the smuggled 2 Host: example.com content to be in chunked encoding. 3 Content-Length:5 4 5.3.3 Body Mutations. For failed HRS attempts using body mu- 5 AAAAA tations, the entrypoint re-wrote the mutated chunked body and therefore did not preserve the mutation. In all cases where Akamai Listing 14: Poisoned request. was an entrypoint, HRS attempts succeeded except for Akamai-ATS. 9 (a) Pairs affected by line mutations. (b) Pairs affected by header mutations. (c) Pairs affected by body mutations. Figure 5: Server pairs affected by request smuggling. ATS ignored the message body part that follows the last chunk as 1.0. Tomcat ignores this header, presumably because it assumes that expected, yet it did not leave that part in the connection. requests with version 1.0 cannot use chunked encoding. As a result, the body of the request shown in Listing 16 is ignored by Tomcat, 5.3.4 Reasoning about Discrepancies. Server developers typically while it is parsed by HAProxy. base their design decisions on official documents like HTTP RFCs. Body Mutations. Listing 17 terminates the chunk extension Unfortunately, RFCs cannot accommodate information about how with an LF rather than a CRLF. NGINX treats the LF the same as to interpret every single iteration of a valid HTTP request. When a CRLF and parses the message body as chunked. However, Aka- confronted with gray areas, developers have to make implementa- mai handles this error by defaulting to Content-Length instead of tion decisions that conform to the RFC, but are not explicit. HRS Transfer-Encoding to parse the message body. arises from this gray area. We illustrate this by providing exam- ples from each part of the HTTP request in our experiments that successfully lead to HRS. 6 IMPACT ASSESSMENT Request Line Mutations. In Listing 15, the version in the re- So far we have answered our core research questions and systemat- quest line is mutated. Despite this mutation, ATS still forwarded ically confirmed that HTTP processing discrepancies lead to novel the message body to an exitpoint. In fact, we observed that ATS HRS vulnerabilities. Next, we present a set of empirical experiments forwarded the message body for all GET requests with any decimal to reaffirm that these vulnerabilities in fact have practical impact, version number (i.e., 99.99). Conversely, this mutation caused Squid and compare our work to existing HRS testing tools. to ignore the message body, presumably because it could not decide what the version is. Squid’s body parsing behavior is dependent on 6.1 Demonstrating Possible Attacks the version, as it ignores request bodies in version 0.9, yet accepts The damage caused by an HRS attack depends on the web applica- them in newer versions. tion and data exposed by the vulnerable server pair. In this paper, Header Mutations. Listing 16 shows a request which uses chun- we do not quantify such damage. Instead, we explore the discrepan- ked encoding with HTTP version 1.0, even though the chunked cies between HTTP processors and quantify the HRS attack surface encoding was introduced to the protocol with version 1.1. Despite independent of the outcome of any particular exploitation scenario. this fact, HAProxy supports chunked encoding in HTTP version Regardless, to demonstrate end-to-end attacks in a proof-of- concept, we set up an environment with a vulnerable application behind a server pair with actionable discrepancies. Specifically, 1 GET / HTTP/ .11 1 POST / HTTP/1. 0 we abused a chunked body parsing discrepancy between Akamai- 2 Host: example.com 2 Host: example.com NGINX. We configured NGINX to serve OWASP Mutillidae [8], a 3 Content-Length: 5 3 Transfer-Encoding: chunked deliberately vulnerable web application for security training. 4 4 We tested three scenarios using HRS: 1) bypassing header rewrit- 5 AAAAA 5 4\r\nBBBB\r\n0\r\n\r\n ing, 2) hijacking requests, and 3) delivering attack payloads. In (1), Listing 15: Line mutation. Listing 16: Header and version. we smuggled a request with an arbitrary X-Forwarded-For value, evading re-writing by the entrypoint. This is critical, since this 1 POST / HTTP/1.1 header is often used in authentication and authorization schemes [26]. 2 Host: example.com 3 Content-Length: 5 In (2) our smuggler request payload constructed a poisoned request 4 Transfer-Encoding: chunked to an attacker-controlled destination, leaking a random user’s re- 5 quest content including session cookies. Finally, in (3) we smuggled 6 4;foo=bar \r \nBBBB\r\n0\r\n\r\n a request which exploits a reflected XSS vulnerability at the desti- nation to have the XSS response delivered to a random user. Videos Listing 17: Body mutation. of these attacks in action are available on the authors’ websites. 10 (a) Pairs affected by line mutations. (b) Pairs affected by header mutations. (c) Pairs affected by body mutations. Figure 6: Failed request smuggling reasons for each server pair. 6.2 Estimating Server Combinations may use distinct proxy technologies, requiring a comprehensive At a first glance, some of the server pairs we test may seem unreal- crawl of each site for an accurate analysis. Finally, a blackbox de- istic for a real-life deployment scenario. However, the Internet has tection methodology cannot determine the placement order of the become a complex ecosystem (and patchwork) of middle-boxes and servers, but only the fact that they are used in some combination. cloud services, where any given request may be processed by not These are non-trivial challenges that we do not tackle in this work. two, but many servers. CDN deployments are prevalent [2], and Figure 7 summarizes our results, showing pairwise server com- multi-CDN chaining is practical [21]. These services themselves binations we observed, where the edge weights represent the inci- may depend on popular proxies, web caches, and web servers (e.g., dence. We find that approximately 17% of the sites among the top Fastly uses Varnish [25], Cloudflare uses NGINX [30]). 10K use technologies that we have identified discrepancies between. To illustrate our point, we conducted an experiment with the top We observe an average of 2.8 technologies, with a median of 3, and 10K sites of the Tranco list [34], exploring what server technolo- a maximum of 5 per site. gies are deployed in the wild. Namely, we visited the homepage of Given the aforementioned limitations, these results represent each site and collected the HTTP response headers. We simultane- a loose lower bound on the incidence of server pairings. Yet, they ously ran route traces for IP addresses seen on path, and performed show two important points. First, out of the 45 possible combina- WHOIS lookups for each. We then searched through this data for tions of the 10 servers in our setup, 36 are used in the wild. Second, known header & value combinations that fingerprint the technolo- seemingly unrealistic combinations are viable, and chained CDNs gies, and for explicit service identifiers inside HTTP responses, are more frequent than other combinations. We conclude that mak- WHOIS data, and email domains. This process resulted in a set of ing presumptions about what server combinations are viable in potential server technologies used for each site. the wild is counterproductive when exploring HRS and similar This methodology has limitations. There is no known way to re- systems-level hazards. Processing discrepancies can crop up on any liably detect proxy services via traffic analysis, particularly because technology, and therefore, all combinations are worth investigating. many services allow operators to strip the identifying headers to prevent fingerprinting. Furthermore, different endpoints on a site 6.3 Comparing T-Reqs to Existing Tools James Kettle’s Burp Suite extension HTTP Request Smuggler [36] and Evan Custodio’s Python script smuggler [7] are the state-of- Squid the-art tools used when testing sites for HRS. Varnish HAProxy Foremost, both of these operate on fundamentally different tar- gets and serve a different purpose than T-Reqs. In particular, these tools are designed for penetration testing of a given target site, Apache Akamai CloudFront treating the entire web deployment as a blackbox, and testing it for a set of known Content-Length and Transfer-Encoding header manipulation attacks presented in the authors’ respective works. Cloudflare In contrast, T-Reqs is not designed to test live sites against known NGINX ATS exploits. T-Reqs tests pairwise combinations of HTTP processors in Tomcat a lab environment, and exercises each component individually, in a greybox manner. It is designed to discover novel HRS vectors, as Figure 7: HTTP processors paired in the wild. This is an unordered opposed to testing a real-life deployment for known attacks. graph, showing pairwise combinations. Red edges indicate pairs that Therefore, these tools are not substitutes for each other. T-Reqs exhibit processing discrepancies, blue edges represent pairs that do serves to discover novel HRS payloads that the others are bound to not. Edge thickness corresponds to the incidence of pairs. miss given their limited scope. That does not diminish the value 11 of previous work. In fact, these tools are suited to work in tandem, While this could be an oversight in design, it is also possible that where T-Reqs finds novel exploits, which can then be added to Burp Akamai’s behavior has changed in the two years since the publica- or smuggler to automate their use in penetration testing. tion of Kettle’s work. We conclude that testing the existing tools in With that in mind, we next describe how these existing tools a large-scale experiment is not safe without explicit penetration- work, and present an empirical study demonstrating T-Reqs’s ability testing agreements. Instead, we conducted our comparative study to create new knowledge for HRS research. in the same test environment used for the experiments with T-Reqs. HRS Detection in Existing Tools. Existing tools use the detec- Empirical Comparison. We have already presented T-Reqs’s tion methodology proposed by Kettle [15]. First, the request in List- output in Section 5. To compare those results with the detections ing 18 is sent to a target to determine if it is affected by a CL.TE dis- from the two existing tools, we ran them in the same experimental crepancy, meaning the entrypoint processes the Content-Length setup. However, there were two cases we could not test. It is not header while the exitpoint prefers Transfer-Encoding. If the tar- possible to run Tomcat in a reverse-proxy mode, and therefore we get is vulnerable, the entrypoint will forward the first four bytes did not test pairs having Tomcat as the entrypoint. Additionally, (i.e., chunk size 1 and chunk data Z), and the exitpoint will timeout we were unable to set up a Cloudflare-CloudFront pair, because the waiting for the next chunk which will never arrive. This timeout Host header rewriting capability necessary for that deployment is delay flags the site as vulnerable. only available for Cloudflare’s Enterprise plan customers [3]. This If the CL.TE test fails, the request in Listing 19 is sent to check limitation was not a factor during our core experiments with T-Reqs; for a TE.CL discrepancy. If there is a vulnerability, a similar delay we had found no discrepancies for this CDN pair, and therefore we can be observed: The entrypoint forwards the body without the did not need to attempt a deployment for exploitability testing. byte X that comes after the last chunk, and the exitpoint receives Unsurprisingly, T-Reqs was the only tool that found the request less content than what Content-Length indicates, therefore timing line and request body related attacks described in Sections 5.2.1 out while waiting for one additional byte. and 5.2.3. The others missed this category of attacks entirely, be- As also emphasized by Kettle, the order of the above two checks cause they are only designed to test for the Content-Length and is important. The TE.CL test should only be performed after con- Transfer-Encoding header manipulation attacks. One exception firming the absence of a CL.TE discrepancy. Otherwise the TE.CL was that Burp found an exploitable Mangled Last-Chunk discrep- request could poison the connection with the byte X in CL.TE- ancy on Akamai-ATS. We manually verified that this was an acciden- impacted targets, launching an attack on arbitrary Internet users. tal true positive, as the request template Burp uses unintentionally All in all, both tools iterate through numerous mutations in the had the trigger for this discrepancy built into the chunked body – Transfer-Encoding header of the requests in Listings 18 and 19, no mutations were necessary for this finding. T-Reqs also detected and check the target for CL.TE and TE.CL discrepancies using the the same vulnerability through body mutations. above methodology. An unanticipated outcome was neither smuggler nor Burp flagged Safety of the Detection Methodology. Unfortunately, we have any request header attacks either, even though they are designed empirically confirmed in our tests that neither tool is currently safe to test for those. We reviewed the source code for both tools and to run on real-world targets. verified that the header modifications they use1,2 indeed do not lead Firstly, Custodio’s smuggler does not follow the above order of to any exploitable HRS vulnerabilities today on the 10 technologies requests which is critical for preventing inadvertent attacks. More in our setup. We attribute this to the fact that these tools repeat interestingly, even though Burp follows the protocol, we have deter- known exploits, and the server vendors have already had two years mined that a false assumption made in the detection methodology to implement mitigations since their disclosure. makes it hazardous for running real-world experiments. Namely, In summary, Burp detected one HRS vulnerability and smuggler the methodology assumes that, when the target is affected by a detected none, whereas T-Reqs yielded all the findings we presented TE.CL discrepancy, the entrypoint will treat the byte Q in Listing 18 in Section 5. We conclude that T-Reqs indeed fulfills its role of as an invalid chunk size and return an error. That will prevent ac- finding novel HRS vectors left out of scope in previous work. cidentally poisoning the connection during the CL.TE check. This assumption does not hold; Akamai servers do not return an error 6.4 Testing HRS in the Wild and forward the request as if Q was a proper chunk size. Due to the aforementioned safety issues, the state-of-the-art HRS detection methodology should not be used outside of specific tar- gets that explicitly allow external testing of their sites. Designing a safe detection scheme likely requires whitebox analysis of different 1 POST / HTTP/1.1 1 POST / HTTP/1.1 HTTP processors to ensure that request queues are not inadver- 2 Host: example.com 2 Host: example.com tently poisoned. Conducting a large-scale HRS measurement in the 3 Transfer-Encoding: chunked 3 Transfer-Encoding: chunked wild safely is a promising future research direction. 4 Content-Length: 4 4 Content-Length: 6 Here, we instead present a preliminary experiment testing real- 5 5 world deployments that has Akamai as entrypoint, and only with 6 1 6 0 a specific HRS payload. This is not an arbitrary choice. Prior to 7 Z 7 8 Q 8 X 1 https://github.com/PortSwigger/http-request-smuggler/blob/master/src/burp/ DesyncBox.java Listing 18: CL.TE test request. Listing 19: TE.CL test request. 2 https://github.com/defparam/smuggler/blob/master/configs/exhaustive.py 12 running the experiment, we carefully studied Akamai’s behavior We stress that our findings should not be taken at face value. and crafted an HRS detection payload, based on Kettle’s approach, This work is not intended to be a prescribed list of vulnerabilities that is guaranteed to be safe for this particular experiment. and their mitigations. We provide strong indicators for hazardous Specifically, we used a body mutation in the Chunk-Size Chunk- server combinations and demonstrate the severity of the issue, so Data Mismatch category. Recall from Section 5.2.3 and Figure 4 that that system owners can vet their environments. this category impacts server pairs that has Akamai as entrypoint, Blame Nobody. We reiterate that HRS is a system interaction and always makes Akamai prefer the Content-Length header. That problem. Individual components of the system are not necessarily enables us to test sites for this novel HRS vulnerability while ac- flawed, but their hazardous combination results in a vulnerability tively avoiding the unsafe situation, where an Akamai server prefers that is not trivial to detect or mitigate. This implies that technology the Transfer-Encoding but allows an invalid chunk size through. vendors are not always in a position to correct these flaws on their To make sure that Akamai is the entrypoint in the target, we own; an ideal HTTP processor that is strictly RFC compliant, using sent a TRACE request with the Max-Forwards:0 header to force a formally-verified parser, and implemented by the best developers the request to stop at the first HTTP server on path even if it does on the planet may still get caught in an HRS attack when combined not support the TRACE method. Out of 861 Akamai customers from with a different technology that interprets a request differently. Tranco Top 10K identified previously in Section 6.2, we were able Unfortunately, the reality is even more complicated, where RFCs to confirm 367 had Akamai as the entrypoint. are ambiguous, bugs are inevitable, and powerful mechanisms to We tested these 367 domains by sending our new HRS detection rewrite HTTP requests are desirable and necessary features. In this request, and flagged sites as vulnerable if they did not respond complex ecosystem, predicting, detecting, mitigating, or fixing HRS within 5 seconds (i.e., the default threshold used in existing tools). is a non-trivial, open research problem. Out of the 367 domains tested, we found 23 to be vulnerable. These While the results we present in this paper may appear to show included a high-profile financial institution, online retailers, and that some technologies and vendors are better than others, that other technology, news, and entertainment sites. is an incorrect interpretation of our results. Our findings do not This experiment is decidedly narrow in scope. However, it suc- represent a meaningful security comparison between the tested cessfully demonstrates that real-world deployments are exposed to servers, and they should not be taken out of context to pit one tech- HRS vulnerabilities we discovered with T-Reqs, despite the many nology against another. Once again, this work presents a scientific, hidden layers of complexity present in the wild that we could not systematic methodology to identify HRS, and uncover previously account for in a lab environment. Designing a generalizable detec- unexplored venues for attacks, so that the developers and users of tion methodology and enabling a full-fledged measurement study these technologies are better equipped to understand the implica- is the logical next step for characterizing the impact of HRS. tions of the issue, and vet their own systems. 8 CONCLUSION This paper is the first systematic exploration of HRS attacks. Re- 7 DISCUSSION visiting our research questions from Section 3, we proposed an As we conclude, we underscore considerations for the correct in- experiment infrastructure and methodology for efficient discovery terpretation of our results. of attacks (Q1), developed a novel grammar-based differential fuzzer Limitations. While this paper represents the most holistic in- to test all components of an HTTP request for viable exploits (Q2), vestigation into HRS to date, it is by no means exhaustive. For provided insights into previously unknown success (and failure) example, we leave non-standard HTTP headers out of scope. There modes enabled by our exploits (Q3), and finally documented haz- are further restrictions we impose on our methodology to make ardous combinations of popular servers (Q4). Our findings collec- the experimentation and analysis feasible, such as limiting the max- tively show that HRS may yet evolve into an even more complex imum number of mutations for an input, and mutating request attack, and it is paramount that the security community tackle the components in isolation in their respective experiment runs. open research questions in the areas of detection and defense. Nonetheless, our approach provides sufficient evidence to ad- dress our research questions, showing that there are indeed vast ACKNOWLEDGMENTS and unexplored opportunities for crafting HRS attacks – and that the security community must stay alert. We make T-Reqs available This work was supported by the National Science Foundation under in the hopes that our fellow researchers will improve on it and grant CNS-1703454 and by Secure Business Austria. make even more exciting discoveries. Real-World Considerations. 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[28] Seyed Ali Mirheidari, Sajjad Arshad, Kaan Onarlioglu, Bruno Crispo, Engin Kirda, Given the expansion of 𝑠 → 𝑡 1 , where 𝑡 1 is a sequence of 𝑛 and William Robertson. 2020. Cached and Confused: Web Cache Deception in characters 𝑐 1𝑐 2 ...𝑐𝑛 , a string mutator is represented as a function the Wild. In USENIX Security Symposium. [29] Seyed Ali Mirheidari, Matteo Golinelli, Kaan Onarlioglu, Engin Kirda, and Bruno 𝑓 (𝑡 1, 𝑜𝑝, 𝑗, 𝑏) where 𝑜𝑝 ∈ {delete-char, replace-char, insert-char}, Crispo. 2022. Web Cache Deception Escalates!. In USENIX Security Symposium. 1 ≤ 𝑗 ≤ 𝑛, and 𝑏 ∈ 𝐵: [30] NGINX. [n.d.]. Cloudflare boosts performance and stability for its millions of websites with NGINX. https://www.nginx.com/success-stories/cloudflare- 𝑐 ...𝑐 𝑐 ...𝑐 , if 𝑜𝑝 = 𝑑𝑒𝑙𝑒𝑡𝑒-𝑐ℎ𝑎𝑟  1 𝑗−1 𝑗+1 𝑛   boosts-performance-stability-millions-websites-with-nginx/.   [31] Hoai Viet Nguyen, Luigi Lo Iacono, and Hannes Federrath. 2019. Your Cache 𝑓 (𝑐 1 ...𝑐𝑛 , 𝑜𝑝, 𝑗, 𝑏) = 𝑐 1 ...𝑐 𝑗−1𝑏𝑐 𝑗+1 ...𝑐𝑛 , if 𝑜𝑝 = 𝑟𝑒𝑝𝑙𝑎𝑐𝑒-𝑐ℎ𝑎𝑟 Has Fallen: Cache-Poisoned Denial-of-Service Attack. In ACM Conference on  if 𝑜𝑝 = 𝑖𝑛𝑠𝑒𝑟𝑡-𝑐ℎ𝑎𝑟  𝑐 1 ...𝑐 𝑗 𝑏𝑐 𝑗+1 ...𝑐𝑛 , Computer and Communications Security.  [32] Shirin Nilizadeh, Yannic Noller, and Corina S. Pasareanu. 2019. DifFuzz: Differ- ential Fuzzing for Side-channel Analysis. In IEEE/ACM International Conference Tree mutation operations. on Software Engineering. Given: [33] Theofilos Petsios, Adrian Tang, Salvatore Stolfo, Angelos D. Keromytis, and Suman Jana. 2017. Nezha: Efficient Domain-Independent Differential Testing. In • 𝑁 is a set of all non-terminal symbols in a CFG. IEEE Security & Privacy. • 𝑇 is a set of all terminal symbols in a CFG. [34] Victor Le Pochat, Tom Van Goethem, Samaneh Tajalizadehkhoob, Maciej Ko- rczyński, and Wouter Joosen. 2021. Tranco – A Research-Oriented Top Sites • 𝐻 is a predefined symbol pool. Ranking Hardened Against Manipulation. https://tranco-list.eu/. Let 𝑠 be a tree mutable symbol which is represented by a CFG as [35] PortSwigger. [n.d.]. Exploiting HTTP request smuggling vulnerabilities. https: //portswigger.net/web-security/request-smuggling/exploiting. [36] PortSwigger. 2019. HTTP Request Smuggler. https://github.com/PortSwigger/ 𝑠 ::= <𝑛 1 >...<𝑛𝑘 > | <𝑛𝑙 >...<𝑛𝑚 > | ... http-request-smuggler. where 𝑠 ∈ 𝑁 , 𝑛𝑖 ∈ 𝑁 for any 1 ≤ 𝑖 ≤ 𝑘 and 𝑙 ≤ 𝑖 ≤ 𝑚. Given the expansion of 𝑠 → <𝑛 1 >...<𝑛𝑘 >, a tree mutator is represented as a 14 function of the symbol 𝑠, an operation 𝑜𝑝, a sequence position 𝑗, ::= "utf-16" | "utf-16BE" | "utf-32" | "utf-32BE" | "us-ascii" | "iso-8859-1" | "utf-7" | "utf-8" and a symbol <ℎ>: ↩→ ::= ::= "Accept-Encoding"  <𝑛 1 >...<𝑛 𝑗−1 ><𝑛 𝑗+1 >...<𝑛𝑘 >, ::= | ::=    if 𝑜𝑝 = 𝑑𝑒𝑙𝑒𝑡𝑒-𝑒𝑙𝑒𝑚 ::= "gzip" | "compress" | "deflate" | "br" | "identity" |    ↩→ "chunked"    <𝑛 >...<𝑛 ><ℎ><𝑛 >...<𝑛 >,   1 𝑗−1 𝑗+1 𝑘 ::= 𝑔(<𝑛 1 >...<𝑛𝑘 >, 𝑜𝑝, 𝑗, <ℎ>) = ::= "Accept-Language"   if 𝑜𝑝 = 𝑟𝑒𝑝𝑙𝑎𝑐𝑒-𝑒𝑙𝑒𝑚 ::= | ::=      <𝑛 1 >...<𝑛 𝑗 ><ℎ><𝑛 𝑗+1 >...<𝑛𝑘 >, ::= "fr" | "en" | "de" ::=     if 𝑜𝑝 = 𝑖𝑛𝑠𝑒𝑟𝑡-𝑒𝑙𝑒𝑚 ::= "Accept-Ranges" ::= "bytes" | "none" where 𝑜𝑝 is the operation type, 1 ≤ 𝑗 ≤ 𝑘, and ℎ ∈ 𝐻 . ::= ::= "Allow" ::= | ::= C FULL GRAMMAR FOR THE REQUEST ::= "ALPN" ::= | HEADERS EXPERIMENT ::= "http%2F1.1" | "h2" ::= The request headers experiment tests all standard HTTP request ::= "Alt-Used" headers together with numerous valid values. Listing 20 shows the : "alternate.example.net" ::= full grammar where an expansion for every 67 header is defined. In ↩→ addition, we present expansions for sub-elements of each header. ::= "Authorization" : "Basic" | "Bearer","Digest","HOBA","Mutual","Negotiate","OAuth", ↩→ "SCRAM-SHA-1","SCRAM-SHA-256","vapid" ::= ::= "123456" | "YWxhZGRpbjpvcGVuc2VzYW1l" ::= ::= ↩→ ↩→ ::= " /_URI_ " ::= "Cache-Control" ::= "HTTP/0.9" | "HTTP/1.0" | "HTTP/1.1" ::= | ::= "GET" | "HEAD" | "POST" | "PUT" | "DELETE" | "CONNECT" | ↩→ ↩→ "OPTIONS" | "TRACE" : "max-age=5" | "max-stale=5" | "min-fresh=5" | "no-cache" | ::= "\r\nHost: _HOST_\r\nConnection:close\r\nX-Request-ID: ↩→ "no-store" | "no-transform" | "only-if-cached" ↩→ _REQUEST_ID_\r\n" ::= ::= | | ::= "CalDav-Timezones" ↩→ | ::= ↩→ ::= "CDN-Loop" ::= | | | ::= | ↩→ | | | | | : "foo123.foocdn.example" | "barcdn.example; trace='abcdef'" | ↩→ | | | | ↩→ "AnotherCDN; abc=123; def='456'" ↩→ | | | ::= ↩→ | | | | | ↩→ ↩→ | | | | | ::= "Content-Encoding" ↩→ | | | | | | ::= ↩→ | | | | ::= "Content-Language" ↩→ | | | | ::= ↩→ | | | | | | ↩→ ↩→ | | | | | ↩→ | | | ::= "Content-Length" ↩→ | | | ::= "40" | "60" | "80" ↩→ | | | | | | ::= ↩→ | | | | ↩→ ::= "\r\n" ::= "Content-Location" ::= ::= | ↩→ "\r\nA\r\nBBBBBBBBBB\r\n0\r\n\r\nBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB : "http://example.com/example" ↩→ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB" ::= "/example" ::= "," ::= ::= ":" ::= "Cookie" ::= ";" : "SID=31d4d96e407aad42" | "PHPSESSID=298zf09hf012fh2; ::= " " ↩→ csrftoken=u32t4o3tb3gg43; _gat=1" ::= "<" ::= ::= ">" ::= "Date" ::= "(" : "Sun, 06 Nov 1994 08:49:37 GMT" | "Sun, 06 Nov 2094 08:49:37 GMT" ::= ")" ::= ::= "=" ::= "Depth" ::= "T" | "F" : "0" | "1" | "infinity" ::= "q=1.0" | "q=0.0" ::= ::= ::= "Destination" ↩→ ::= : "chunked" ::= "Early-Data" ::= : "1" ::= "Accept" ::= ::= | ::= "Expect" ::= : "100-continue" ::= "*/*" | "application/octet-stream" | "application/pdf" | ::= ↩→ "application/pkcs8" | "application/zip" | "audio/mpeg" | "audio/vorbis" | ::= "Expires" ↩→ "audio/example" | "font/woff" | "font/ttf" | "font/otf" | "image/jpeg" | ::= ↩→ "image/png" | "image/svg+xml" | "model/3mf" | "text/html" | "video/mp4" ::= "Forwarded" ::= ::= "by" ::= "Accept-Charset" ::= ::= | ::= "From" ::= ::= "webmaster@w3.org" 15 ::= ::= ::= "HTTP2-Settings" ↩→ ::= "AAMAAABkAARAAAAAAAIAAAAA" ::= "Sec-Websocket-Accept" ::= ::= "s3pPLMBiTxaQ9kYGzzhZRbK+xOo=" ::= "If" ::= ::= ↩→ ::= "urn:uuid:181d4fae-7d8c-11d0-a765-00a0c91e6bf2" | ::= "Sec-Websocket-Extensions" ↩→ "urn:uuid:58f202ac-22cf-11d1-b12d-002035b29092" ::= | : ↩→ ::= ::= "deflate-stream" | "mux" | "max-channels:4; ::= "If-Match" ↩→ flow-control" ::= | ::= : "*" | "'xyzzy'" ↩→ ::= ::= "Sec-Websocket-Key" ↩→ ::= "dGhlIHNhbXBsZSBub25jZQ==" ::= "If-Modified-Since" ::= ::= ↩→ ::= "If-None-Match" ::= "Sec-Websocket-Protocol" ::= ::= | ::= "If-Range" ::= | ↩→ ::= ::= "chat" | "superchat" ↩→ ::= ::= "If-Schedule-Tag-Match" ↩→ ::= ::= "Sec-Websocket-Version" ↩→ ::= "13" ::= "If-Unmodified-Since" ::= ::= ::= "Slug" ::= "The Beach at S%C3%A8te" ::= "Link" ::= ::= ::= ::= "TE" ::= | ↩→ ::= ::= "Max-Forwards" ::= "gzip" | "compress" | "deflate" | "br" | "identity" | : "0" | "1" ↩→ "chunked" | "trailers" ::= ::= ↩→ ::= "Timeout" ::= "MIME-Version" ::= | : "1.0" | "1.1" ::= "Infinite" | "Second-4100000000" ::= ::= ↩→ ::= "Topic" ::= "OData-Isolation" ::= "upd" : "snapshot" ::= ::= ::= "Trailer" ↩→ ::= "Expires" ::= "OData-MaxVersion" ::= : "4.0" ↩→ ::= ::= "Transfer-Encoding" ↩→ ::= | ::= "OData-Version" ::= ::= ::= "TTL" ↩→ ::= "0" | "1" ::= "Ordering-Type" ::= ::= "DAV:unordered" | "DAV:custom" | ::= "Urgency" ↩→ "http://example.org/example.html" ::= "very-low" | "low" | "normal" | "high" ::= ::= ::= "Origin" ::= "Upgrade" ::= "http://example.com" | "null" ::= | ::= ::= "websocket" | "HTTP/2.0" | "SHTTP/1.3" | "IRC/6.9" | ::= "OSCORE" ↩→ "RTA/x11" ::= "CSU" | "AA" ::= ::= ::= "User-Agent" ::= "Overwrite" ::= "curl/7.16.3 libcurl/7.16.3 OpenSSL/0.9.7l zlib/1.2.3" ::= ::= ::= "Position" ::= "Via" : "first" | "last" | "after example.html" ::= | ::= ::= "1.0 fred" | "1.1 p.example.net" ::= "Pragma" : "no-cache" ::= Listing 20: The full grammar for the request headers experiment. ::= "Prefer" ::= | : "respond-async" | "wait=100" | "handling=lenient" ::= ↩→ ::= "Proxy-Authorization" ::= ::= "Range" ::= "5-8" | "5-" ::= ::= "Referer" ::= ::= "Schedule-Reply" ::= ↩→ ::= "Sec-Token-Binding" ::= "AIkAAgBBQLgtRpWFPN66kxhxGrtaKrzcMtHw7HV8" 16