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title: "SerialDetector: Principled and Practical Exploration of Object Injection Vulnerabilities for the Web"
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title: "SerialDetector: Principled and Practical Exploration of Object Injection Vulnerabilities for the Web"
author: Mikhail Shcherbakov, Musard Balliu
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- Musard Balliu
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# SerialDetector: Principled and Practical Exploration of Object Injection Vulnerabilities for the Web
**SerialDetector: Principled and Practical Exploration of Object Injection Vulnerabilities for the Web** - Mikhail Shcherbakov, Musard Balliu, NDSS Symposium.
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SerialDetector: Principled and Practical Exploration
of Object Injection Vulnerabilities for the Web
Mikhail Shcherbakov Musard Balliu
KTH Royal Institute of Technology KTH Royal Institute of Technology
mshc@kth.se musard@kth.se
Abstract—The last decade has seen a proliferation of code- the law firms filed 23 class-action lawsuits, which would
reuse attacks in the context of web applications. These at- make it the largest suit in US history. The breach rooted in
tacks stem from Object Injection Vulnerabilities (OIV) enabling insecure deserialization in the Apache Struts framework within
attacker-controlled data to abuse legitimate code fragments a Java web application, which led to remote code execution
within a web application’s codebase to execute a code chain (RCE) on Equifax web servers. The attack exploited the XML
(gadget) that performs malicious computations, like remote code
serialization of complex data objects into textual strings to
execution, on attacker’s behalf. OIVs occur when untrusted data
is used to instantiate an object of attacker-controlled type with introduce malicious XML payloads into Struts servers during
attacker-chosen properties, thus triggering the execution of code the deserialization process [46]. These attacks motivate the
available but not necessarily used by the application. In the need for studying code-reuse vulnerabilities systematically.
web application domain, OIVs may arise during the process
of deserialization of client-side data, e.g., HTTP requests, when
Object Injection Vulnerabilities. In web applications,
reconstructing the object graph that is subsequently processed Object Injection Vulnerabilities (OIV) occur when an attacker
by the backend applications on the server side. can arbitrarily modify the properties of an object to abuse the
data and control flow of the application. For example, OIVs
This paper presents the first systematic approach for de- may arise during the deserialization of data from the client
tecting and exploiting OIVs in .NET applications including the side, e.g., HTTP requests, when reconstructing the object graph
framework and libraries. Our key insight is: The root cause of
OIVs is the untrusted information flow from an application’s
that is subsequently processed by the backend applications on
public entry points (e.g., HTTP request handlers) to sensitive the server side. Similarly to classical exploits such as return-
methods that create objects of arbitrary types (e.g., reflection oriented programming (ROP) and jump-oriented programming
APIs) to invoke methods (e.g., native/virtual methods) that trigger (JOP), which target memory corruption vulnerabilities [8],
the execution of a gadget. Drawing on this insight, we develop [36], [45], OIVs enable attacker-controlled data to trigger the
and implement SerialDetector, a taint-based dataflow analysis execution of legitimate code fragments (gadgets) to perform
that discovers OIV patterns in .NET assemblies automatically. malicious computations on attacker’s behalf. The following
We then use these patterns to match publicly available gadgets requirements are needed to exploit an OIV [32]: (i) the attacker
and to automatically validate the feasibility of OIV attacks. controls the type of the object to be instantiated, e.g., upon
We demonstrate the effectiveness of our approach by an in- deserialization; (ii) the reconstructed object calls methods in
depth evaluation of a complex production software such as the
Azure DevOps Server. We describe the key threat models and
the application’s scope; (iii) there exists a big enough gadget
report on several remote code execution vulnerabilities found by space to find types that the attacker can chain to get an RCE.
SerialDetector, including three CVEs on Azure DevOps Server. Existing works show that OIVs are present in mainstream
We also perform an in-breadth security analysis of recent publicly programming languages and platforms like Java [24], [33],
available CVEs. Our results show that SerialDetector can detect JavaScript [28], PHP [17], .NET [18], [32], and Android [34].
OIVs effectively and efficiently. We release our tool publicly to
support open science and encourage researchers and practitioners Challenges. Despite the high impact of OIV, efforts on
explore the topic further. tackling their root cause have been unsatisfactory. A witness is
the fact that a decade after the discovery of these vulnerabilities
a comprehensive understanding of languages features at the
I. I NTRODUCTION
heart of OIVs has yet to emerge. One result is the ongoing
The last decade has seen a proliferation of code-reuse arms race between researchers discovering new attacks and
attacks in the context of web applications [9], [13], [17], [18], gadgets and vendors providing patches in an ad-hoc manner.
[24], [28], [33]. The impact of these attacks can be devastating. To date, the best efforts in discovering and exploiting OIVs
The recent attack that hit the credit reporting agency Equifax have been put forward by the practitioners’ community [17],
exposed the personal information (credit card numbers, Social [18], [22], [32]. Except for a few recent works [13], [23],
Security numbers) of 143 million US consumers. As a result, [25], [28], [31], the problem remains largely unexplored in
the academic community. Most existing works address OIVs
within the general context of injection vulnerabilities, thus
Network and Distributed Systems Security (NDSS) Symposium 2021 lacking targeted techniques for detection and exploitation in
21-25 February 2021, Virtual web applications [6], [9], [43], [47].
ISBN 1-891562-66-5
https://dx.doi.org/10.14722/ndss.2021.24550 A principled investigation of OIVs in real-world applica-
www.ndss-symposium.org tions requires analyzing not only the applications, but also
the underlying framework and libraries that these applications these threat models, we show SerialDetector in action to
build on. In fact, most of the known attacks stem from weak- identify and exploit highly-critical vulnerabilities leading to
nesses in frameworks and libraries. This is challenging task remote code execution on the server.
since production scale frameworks, e.g., the .NET Framework,
are complex entities with large codebases, intricate language II. T ECHNICAL BACKGROUND
features, and lack of source code. Existing approaches rely
on static source code analysis of applications and ignore This section provides background information and illumi-
frameworks and libraries. Moreover, they focus on a whitelist nates the core security issues with OIVs in .NET applications.
of magic methods [13], [17], i.e., vulnerable APIs at the We identify the key ingredients in the lifecycle of an OIV,
application level, thus missing attacks that may be present in distinguishing between application-level OIVs (Section II-A)
unknown methods using the same features at the framework and infrastructure-level OIVs (Section II-B). Appendix A
level. Another key challenge is the lack of automation and provides a brief overview of the .NET Framework.
open source tools to investigate the feasibility of potential
attacks. While state-of-the-art countermeasures against OIVs A. Application-level OIVs
rely on blacklisting/whitelisting techniques [5], [10], [23], [25],
Applications can be vulnerable to OIVs whenever untrusted
[27], [31], [39], [40], it is essential to develop tools that check
data instantiates an object of arbitrary type and subsequently
feasibility of attacks in a principled and practical manner.
influences a chain of method calls resulting in the execution
Contributions. This work presents the first systematic ap- of a dangerous operation. For an attack to be successful, the
proach for detecting and exploiting OIVs in .NET applications, following ingredients are required: (1) a public entry point
including the .NET Framework and third-party libraries. Our allowing the attacker to inject untrusted data; (2) a sensitive
key observation is that the root cause of OIVs is the untrusted method creating an object of attacker-controlled type; (3) a
information flow from an applications’ entry points to sensitive gadget consisting of a chain of method calls that ultimately
sinks that create objects of arbitrary types to invoke attack execute a dangerous operation; (4) a malicious payload trig-
triggers that initiate the execution of a gadget. Drawing on gering the execution of steps (1)-(3).
this insight, we develop and implement SerialDetector [41], a
tool for detecting OIV patterns automatically and exploiting Consider a C# implementation of the classical Command
these patterns based on publicly-available gadgets in a semi- design pattern [20] for a smart home controller (Listing 1).
automated fashion. Following the line of work on static analy- The controller implements the method CommandAction as
sis at bytecode level [4], [7], [15], [21], [47], [48], SerialDetec- an entry point handling HTTP POST requests. Following the
tor implements an efficient and scalable inter-procedural taint- design pattern, a developer creates an object of type name
based static analysis targeting .NET’s Common Intermediate dynamically using the method Activator.CreateInstance
Language. At the heart of our approach lies a field-sensitive of the .NET Framework. Subsequently, the code calls the
and type-sensitive data flow analysis [42], [47] that we leverage virtual method Execute to execute the command specified in
to analyze the relevant object-oriented features and detect the input parameter args, e.g., a Backup command that runs
vulnerable patterns. We evaluate the feasibility of our approach a database backup. The main benefit of this design pattern is
on 15 deserializers reporting on the efficiency and effectiveness that a developer can define new commands without changing
of SerialDetector in generating OIV patterns. We conduct the implementation of the method CommandAction. This can
an in-depth security analysis of production software such as be achieved by simply adding a new class that implements the
the Azure DevOps Server and find three RCE vulnerabilities. interface ICommand.
To further evaluate SerialDetector, we perform an in-breadth public class SmartHomeController : Controller {
security analysis of recent .NET CVEs from public databases
[HttpPost]
and report on the effort to analyze and reproduce these exploits.
In summary, the paper offers the following contributions: public ActionResult CommandAction(string name, string
args) {
• We identify the root cause of Object Injection Vulnerabilities
var t = Type.GetType(name);
and present a principled and practical approach to detect
such vulnerabilities in a framework-agnostic manner. var c = (ICommand) Activator.CreateInstance(t);
• We present the first systematic approach for detecting and c.Execute(args);
exploiting OIVs in .NET applications including the frame- return RedirectToAction("Index");
work and libraries. }}
• We develop SerialDetector [41], a practical open source public class Backup : ICommand {
tool implementing a scalable taint-based dataflow analysis
public virtual void Execute(string parameters) {
to discover OIV patterns, as well as leveraging publicly
available gadgets to exploit OIVs in real-world software. DB.Backup(parameters);
• We perform an thorough evaluation of OIV patterns in .NET- }}
based deserialization libraries showing that SerialDetector
can find vulnerable patterns with low burden on a security Listing 1: Implementation of Command pattern
analysis. We use these patterns in an in-breadth security
analysis of vulnerable applications to show that SerialDe- Unfortunately, such flexible design comes with security
tector can help uncovering OIVs effectively and efficiently. issues. Consider the class OSCommand implementing the same
• We carry out an in-depth security analysis of Azure DevOps interface ICommand to run a process based on the data from
Server illuminating the different threat models. Drawing on parameters (Listing 2). The method Execute splits the
2
input parameters to extract the actual OS command and its created object has the expected type T. However, the type cast
arguments before the call to Process.Start. is executed only after the creation of the object graph, hence
the system will still create objects based on the information
public class OSCommand : ICommand { from YAML data with no restriction on the type.
public virtual void Execute(string parameters) {
public T Deserialize(string yaml) {
var firstSpace = parameters.IndexOf(’ ’);
var rootNode = GetRootNode(yaml);
var command = parameters.Substring(0, firstSpace);
return (T) DeserializeObject(rootNode);
var args = parameters.Substring(firstSpace + 1);
}
Process.Start(command, args);
private object DeserializeObject(YamlNode node) {
}}
var type = GetTypeFrom(node);
Listing 2: Implementation of OSCommand var result = Activator.CreateInstance(type);
foreach (var nestedNode in GetNestedNodes(node)) {
A developer might not even be aware of the existence of var value = DeserializeObject(nestedNode);
OSCommand in the modules loaded by the application. An
var property = GetPropertyOf(nestedNode);
attacker can use the class type OSCommand as a parameter
property.SetValue(result, value);
to the POST request to create an OSCommand object and
execute malicious commands in the target OS. For example, }
a payload in a POST request body with two parameters, return result;
name = OSCommand and args = del /q * results in re- }
mote code execution, deleting all files in the current directory.
Listing 3: Implementation of YAML deserializer
Observe that the above-mentioned OIV fits our tem-
plate: The application exposes a public entry point
(CommandAction) to call a sensitive method creating an object The method DeserializeObject creates an object
of attacker-controlled type (Activator.CreateInstance). of the type specified by the YAML node and sets its
Subsequently, it uses the object to trigger the execution of a fields’ properties recursively. It uses a .NET Reflection
gadget (method Execute of class OSCommand) via a malicious API to create object by a type defined at runtime (via
payload. To detect such attacks, a comprehensive analysis Activator.CreateInstance) and executes a setter method
should consider all implementations of the method Execute for each property (via PropertyInfo.SetValue). An at-
in classes implementing the ICommand interface. tacker can find gadgets in the target system, i.e., the .NET
Framework and third-party libraries, that allow executing
malicious actions in their property setter. For example, the
B. Infrastructure-level OIVs class ObjectDataProvider can be used as gadget for the
OIVs can be present at the level of the infrastructure that YamlDotNet deserializer and any other deserializer that allows
supports applications running on the server side. For .NET the execution of property setters for arbitrary classes.
technologies, the infrastructure includes the .NET Framework
public class ObjectDataProvider {
and libraries (see Appendix A). A prime example of OIVs at
public object ObjectInstance {
the infrastructure layer is insecure deserialization. Deserializa-
tion is the process of recreating the original state of an object set {
from a stream of bytes that was produced during a reverse this._objectInstance = value;
process called serialization. In the web domain, serialization this.Refresh();
can be used to convert an object from the client side to a stream }}
of bytes that can be transmitted over the network and used to
public void Refresh() {
recreate the same object on the server side. To achieve this, the
deserializer may instantiate objects based on metadata from the /*...*/
serialized stream. Thus, an attacker can create an object of an obj = this._objectType.InvokeMember(
arbitrary type by manipulating the metadata in the serialized this.MethodName, /*...*/,
stream, which may cause the deserializer to execute dangerous this._objectInstance, this._methodParameters);
methods of the object.
}}
We illustrate OIVs in insecure deserialization with a run-
ning example which we will discuss further in Section III. We Listing 4: Implementation of class ObjectDataProvider
consider the YamlDotNet library that implements serialization
and deserialization of data in the YAML format. Listing 3 Listing 4 shows a snippet of the class
shows the simplified code fragment used by YamlDotNet to ObjectDataProvider. The property setter of the object
deserialize data obtained via the parameter yaml. The method ObjectInstance calls the method Refresh which in
Deserialize is a public entry point that may receive data turn invokes the method specified in MethodName using
from untrusted sources like HTTP request parameters, cookies, the .NET Reflection API. Hence, the attacker controls
or files uploaded to a web application. The method parses the properties ObjectDataProvider.MethodName and
the input and calls the method DeserializeObject with ObjectDataProvider.ObjectInstance enabling the
the root YAML node as input. A type cast ensures that the execution of arbitrary methods.
3
YamlDotNet
To run arbitrary commands during YAML deserializa-
tion process, e.g. a calculator, an attacker leverage the class Deserializer.Deserialize()
ObjectDataProvider to create a payload as in Listing 5.
Specifically, the deserializer will execute the property set- Deserializer.DeserializeObject()
ter ObjectDataProvider.ObjectInstance and invoke the
method Process::Start to run calc.exe. System System.Reflection
! { Activator.CreateInstance() PropertyInfo.SetValue()
MethodName: Start,
ObjectInstance: RuntimeTypeHandle.Allocate() RuntimeMethodInfo.UnsafeInvokeInternal()
! {
RuntimeMethodHandle.InvokeMethod()
StartInfo:
! {
FileName: cmd,
Fig. 1: OIV pattern for YamlDotNet Deserializer: public entry
point (green), sensitive sink (red), and attack trigger (blue)
Arguments: ’/C calc.exe’
}}}
Listing 5: YAML payload of ObjectDataProvider The method Activator.CreateInstance performs a
sequence of method calls which results in executing the native
The YamlDotNet’s OIV follows our template: The library method RuntimeTypeHandle.Allocate(type). This
exposes a public entry point (Deserialize) to call a sen- method takes as input a parameter type and uses it to define
sitive method creating an object of attacker-controlled type the type of the returned object. We call such methods sensitive
(Activator.CreateInstance). Subsequently, it uses the sinks. In general, sensitive sinks are either native (external)
object to trigger the execution of a gadget (the property setter methods or run-time generated methods that return an object
of class ObjectDataProvider) via a malicious payload. To of the type specified in their input parameter. The .NET
detect such vulnerabilities, a comprehensive analysis should Framework contains in total 123 sensitive sinks. A similar
consider all implementations of the property setter methods analysis of the method SetValue shows that the subsequent
like SetValue in the codebase of the .NET Framework sequence of method calls results in executing the native method
and libraries. Observe that the analysis should target .NET RuntimeMethodHandle.InvokeMethod(obj,..., sig),
assemblies to account for OIVs in the framework and libraries. which invokes the method sig of object obj. Hence, an
attacker controlling the type of the object obj and the name of
III. OVERVIEW OF THE A PPROACH the method sig can execute arbitrary code as in our example.
We call such methods attack triggers since they determine
This section discusses the key insights of our approach the first method of a gadget chain that leads to malicious
(Section III-A) and provides a high-level overview of the behavior. In fact, an attack trigger puts the system into a
architecture and workflow of SerialDetector (Section III-B). state that does not meet the specification as intended by the
developer. Other potential candidates for attack triggers are
A. Root cause of Object Injection Vulnerabilities virtual method calls, e.g., the method Execute in Listing 1,
We now take a closer look at the vulnerability of which enable attackers to execute concrete implementations
YamlDotNet library in Section II-B. Listing 3 shows that of these methods at their choice.
the vulnerability occurs because of an insecure chain of In light of this analysis, we identify the root cause of an
method calls during the deserialization of attacker-controlled OIV based on three ingredients: (a) public entry points; (b)
data. The chain starts from a call to the public method sensitive sinks; and (c) attack triggers. We use these ingredients
Deserialize(yaml) which uses the untrusted input in to compute OIV patterns in large codebases. We define an OIV
variable yaml to create an object of arbitrary type via the pattern as a public entry point that triggers the execution of
method Activator.CreateInstance and subsequently use a sensitive sink to create an object that controls the execution
it to call the method SetValue. The latter executes the code of of an attack trigger. Figure 1 depicts the OIV pattern for our
a property setter of the created object using a property name. running example in Section II-B. Motivated by our notion of
The vast majority of related works leverage publicly avail- OIV pattern, we address three additional key questions: (ii)
able knowledge about signatures of vulnerable methods, like Can we provide practical tool support to detect OIV patterns
Activator.CreateInstance and SetValue, to identify in large-scale applications including frameworks and third-
such (magic) methods in a target codebase [13], [18], [32], party libraries? (iii) How do we validate the usefulness of
[33]. These works rely on the knowledge of vulnerable method the generated patterns? (iv) Are there real-world applications
signatures to either build or reuse malicious gadgets. We argue to give evidence for the feasibility of the approach?
that such syntax-based approaches are not ideal as modern
applications may hide unknown methods that achieve the same B. SerialDetector
malicious effect. This leads us to the first research question:
(i) What is an appropriate criteria for identifying OIVs? To Overview of SerialDetector. We have developed a static
help answering this question, we dive deeper into the analysis analysis tool, dubbed SerialDetector [41], to detect and ex-
of the two vulnerable methods of our example. ploit Object Injection Vulnerabilities in .NET applications and
4
Detection Phase
at performing an in-depth security evaluation of our approach
Sensitive Sinks
Call Graph Entry Point Data Flow on production software such as Microsoft Azure DevOps for
.NET Assemblies
Analysis Detection Analysis which the source code is not available. Fourth, CIL has fewer
language constructs that must be supported by the analyzer as
Patterns
compared to the high-level languages. By focusing on CIL,
Exploitation Phase we do not lose any significant data that is relevant to our code
Knowledge Base
analysis. In fact, CIL is a type-safe language with complete
type information in the metadata. On the other hand, CIL
Gadgets Populating Payload/ inherits well-known challenges for the analysis of stack-based
Matching Knowledge Template
Base Generation object-oriented intermediate languages, e.g., the emulation of
the evaluation stack and the reconstruction of control flow.
We develop and implement a principled and practical
Application Call Graph Exploit Template
Analysis Generation Validation
field-sensitive taint-based dataflow analysis targeting the CIL
language. In Section IV we present the details of the analysis
Vulnerabilities
for a core of CIL instructions. At the heart of this analysis
lies a modular inter-procedural abstract interpretation based
on method summaries, pointer aliasing, and efficient on-the-fly
Fig. 2: Architecture and workflow of SerialDetector: reconstruction of the control flow graph. We present the algo-
automated steps (green) and manual steps (blue) rithms underpinning our analysis in a principled manner and
discuss various challenges and solutions related to low-level
language features. The analysis implements type-sensitivity, a
lightweight form of context-sensitivity, and a type-hierarchy
libraries. Figure 2 describes the architecture and workflow of graph analysis for reconstruction of the call graph. We find
SerialDetector. At high level, the tool operates in two phases: that these features provide a middle ground to implementing
A fully-automated detection phase and a semi-automated ex- scalable yet precise algorithms for detecting OIV patterns.
ploitation phase. In the detection phase, SerialDetector takes Similar analysis have been implemented in the context of
as input a list of .NET assemblies and a list of sensitive sinks, web applications [43], [47] and mobile applications [4], [21].
and performs a systematic analysis to generate OIV patterns While these analysis leverage intermediate languages featuring
automatically. The exploitation phase matches the generated control flow and call graph reconstruction (e.g., FlowDroid
patterns with a publicly available list of gadgets. When a gad- builds on the SOOT framework [48]), SerialDetector imple-
get matches a pattern, we describe the gadget in a knowledge ments these features on the fly.
base to generate malicious payloads for different formats. The
entry points of the matched pattern allow us to describe tem- Roadmap of results. In Section V, we discuss our im-
plates in the knowledge base. Populating the knowledge base plementation of SerialDetector including challenges and lim-
is a manual operation; the payload and template generation is itations. Following Figure 2, the detection phase performs a
performed automatically based on the described rules. For a call graphs analysis for a set of input assemblies, e.g., the
target application, SerialDetector performs a lightweight call .NET Framework and third-party libraries, to identify public
graph analysis to identify control flow paths that make use entry points that may reach sensitive sinks. Then, it uses such
of the vulnerable templates described in the knowledge base. information to carry out the dataflow analysis to identify attack
Subsequently, it uses the automatically generated payloads to triggers, thus generating a list of OIV patterns. However, the
validate their exploitability for the target application during the usefulness of the generated patterns depends on the existence
exploit generation step. The exploit generation may require of matching gadgets that result in exploits. While gadget
modifying the payload and other application inputs, or a generation is orthogonal to pattern generation, we evaluate
combination of multiple vulnerabilities into one exploit. This is SerialDetector by analyzing .NET deserialization libraries with
a manual step requiring knowledge of the application’s threat publicly available gadgets [3]. Because an attack trigger is
model and analysis of the data validation code, e.g., dynamic the first method in a gadget, it is sufficient that an attack
analysis or application debugging. SerialDetector does not trigger from our generated patterns matches the first method
automate this process, but provides aids such as automated of a gadget. Subsequently, we validate the feasibility of these
validation of modified payload on a vulnerable template and attacks using our payload generator. In Section VI, we discuss
automated generation of the call graph. We explain both the details of our evaluation showing that SerialDetector finds
phases in detail in Section V-A. In Section VII, we use the patterns associated with vulnerable deserializers.
vulnerabilities found in the Azure DevOps Server to showcase
the exploit generation and validation process. While these results show that SerialDetector is useful in
detecting OIV patterns in the .NET Framework and its deserial-
Static analysis. SerialDetector targets the Common Inter- ization libraries, as well as in generating and validating exploits
mediate Language (CIL) instead of working with the source for known gadgets, it is unclear whether these vulnerabilities
code such as C#. This choice is motivated by several reasons: appear in production software. In fact, an application build
First, we aim at analyzing the code of the .NET Framework on top of the .NET Framework and libraries might still use a
to identify sensitive methods which are not available at the vulnerable deserializer in a secure manner, e.g., by performing
source level. Second, this approach allows us to implement a validation of the untrusted input. To validate this claim, we
framework-agnostic analysis without any knowledge about the use SerialDetector to carry out a comprehensive in-breadth
known vulnerable methods of the framework. Third, we aim security analysis of vulnerable .NET applications (Section VI)
5
and an in-depth security analysis of the Azure DevOps Server The execution model consists of configurations cf g ∈
(Section VII). We report on the number of false positive and Conf of shape cf g = (pc, cs, E, h, s) containing the program
false negatives of our analysis, and on the number of manual counter pc ∈ PC , environment E ∈ Env , heap h ∈ Heap,
changes of exploit candidates to generate a successful payload. call stack cs = (pc, E, s)∗ with cs ∈ (PC × Env × Val ∗ )∗ ,
and stack s ∈ Val ∗ . We write to denote an empty stack
In Section VII we use SerialDetector’s call graph analysis and t :: v to denote a stack with top element v and tail t.
to identify control flow paths from public APIs of the Azure The semantics of CIL programs is defined by the transition
DevOps Server to vulnerable entry points in the .NET Frame- relation →∈ Conf ×Conf over configurations, using the rules
work. By exploring different threat models in the applica- in Figure 12. As expected, the reflexive and transitive closure
tion, SerialDetector found three critical security vulnerabilities →∗ of → induces a set of program executions. Notice that the
leading to Remote Code Execution in Azure DevOps Server. program P is fixed, hence the instruction to be executed next
In line with the best practices of coordinated disclosure, we is identified by the program counter pc. The semantics of CIL
reported the vulnerabilities to the affected vendors. Microsoft is standard and we report it in Figure 12 in Appendix.
recognized the severity of our findings and assigned CVEs
to all three exploits. We also received three bug bounties B. Intra-procedural dataflow analysis
acknowledging our contributions to Microsoft’s security.
We now present our intra-procedural dataflow analysis
IV. TAINT-BASED S TATIC A NALYSIS based on abstract interpretation of CIL instructions. Motivated
by the root cause of OIVs, our abstraction overapproximates
This section presents a taint-based static analysis underpin- operations over primitive types and focuses on tracking the
ning the detection phase of SerialDetector. The analysis targets propagation of object locations from sensitive sinks to attack
CIL, an object-oriented stack-based binary instruction set, and triggers. Our symbolic analysis combines aliases’ computation
it features a modular inter-procedural field-sensitive dataflow with taint tracking [37], [38] using a store-based abstraction
analysis that we leverage to detect OIV patterns for large of the heap [26]. We present the key features of the analysis
code. We provide an overview of the core language features implemented in SerialDetector via examples and principled
(Section IV-A), and discuss challenges and solutions for imple- rules underpinning our algorithms.
menting a precise, yet scalable, intra-procedural (Section IV-B)
Our abstract interpretation of CIL instructions leverages
and inter-procedural analysis (Section IV-C).
a symbolic domain of values for object locations and other
primitive values. Abusing notation, we assume a set of sym-
A. CIL language and notation bolic values Val = Loc ∪ Sv containing symbolic locations
l ∈ Loc and other symbolic values sv ∈ Sv . The latter
CIL is a stack-based language running on the CLR virtual
is used as a placeholder to abstract away operations over
machine (see Appendix A). We focus on a subset of instruc-
primitive datatypes. We use symbolic configurations of shape
tions to describe the core ideas of our analysis.
hpc, E, h, s, φ, ψi where the first four components correspond
Inst ::= ldvar x | ldfld f | stvar x | stfld f | newobj T | to symbolic versions of the concrete counterparts, while φ and
br i | brtrue i | call i | ret ψ overapproximate symbolic stacks and control flow.
Challenges and solutions at high level. Symbolic analysis
We assume a set of variables x, y, args, · · · ∈ Var con- for stack-based languages like CIL requires tackling several
taining root variables, i.e., formal parameters of methods, challenges related to: (a) abstract representation of the heap;
and local variables; a set of object fields f, g, · · · ∈ Fld ; a (b) unstructured control flow and symbolic representation of
set of values v, l, · · · ∈ Val consisting of object locations the stack; (c) sound approximation of control flow, e.g, loops.
l, l1 , · · · ∈ Loc ⊆ V al and other values, e.g., booleans true
and f alse; a set of class types C, T ∈ Types. We write We address these challenges using a store-based abstraction
f [x 7→ v] for substitution of value v for parameter x in function of the heap and an efficient on-the-fly computation of merge
f and f (x) for the value of x in f . We use f (x)↓ to represent points for conditionals and loops via forward symbolic anal-
that the partial function f is defined in x, and f (x)↑ otherwise. ysis. Our analysis is flow-insensitive, hence the abstract heap
We write (b ? e1 : e2 ) to denote a conditional expression graph and information about aliases holds at any program point
returning e1 if the condition b is true, e2 otherwise. within a method. While some code may be traversed twice to
account for jump instructions, we ensure that the code is only
The memory model contains an environment E : Var 7→ analyzed once. Moreover, we ensure the consistency of the
Val mapping variables to values, a heap h : Loc × Fld 7→ Val symbolic stack by recording the stack state for every branch
mapping object locations and fields to values, an (operand) instruction and combining the stacks at merge points, while
stack s and a call stack cs. The environment and heap map- updating the pointers in the heap and environment.
pings are partial functions, hence we write ⊥ for the undefined
Abstracting the heap. We represent the heap as a directed
value. A program P ∈ Prog consists of a list of instructions
graph where nodes denote abstract locations in the memory and
Inst∗ indexed by a program counter index pc, i ∈ P C. We
edges describe points-to relations between symbolic locations.
tacitly assume there is set of class definitions including a set
Edges contain labels corresponding to the fields and variables
of fields and a set of methods, and a distinguished method to
connecting the two locations. Here, the graph is computed from
start the execution. Each method definition includes a method
the symbolic environment and the symbolic heap.
identifier with formal parameters and the list of instructions.
We write sig ∈ Sig for the signature of a method which Figure 3 depicts the abstract semantics of the heap. For
consists of the method’s name and its formal parameters. simplicity, we assume that the environment E and the heap h
6
S-S T VAR
P (pc) = stvar x arg arg
(E 0 , h0 , s0 , φ0 ) = update(sv, E(x), E, h, s, φ)
hpc, E, h, s :: sv, φ, ψi → hpc + 1, E 0 , h0 , s0 , φ0 , ψi la next lb lab next
S-S T F LD
P (pc) = stfld f obj obj obj
(E 0 , h0 , s0 , φ0 ) = update(h(l, f ), sv, E, h, s, φ)
hpc, E, h, s :: sv :: l, φ, ψi → hpc + 1, E 0 , h0 , s0 , φ0 , ψi lc ld lcd
(a) Before merging (b) After merging
Fig. 3: Abstract interpretation of heap
Fig. 4: Graph representation of symbolic heap
are initialized to fresh symbolic values sv ∈ Sv , hence E(x)
and h(l, f ) are always defined. Rules S-L DVAR, S-L D F LD, and the preservation of symbolic stack’s consistency across
and S-N EW O BJ (not shown) are similar to the corresponding different branches. We implement an analyses that does not
rules in Figure 12 but operate on symbolic values and ignore require reconstructing of the CFG explicitly. Specifically, we
the call stack cs. Rules S-S T VAR and S-S T F LD rely on an analyze instructions "sequentially" following the program or-
update function to implement the flow-insensitive and field- der imposed by the program counter pc and ensure consistency
sensitive abstract semantics. This function takes as input two of the symbolic stack and the heap on-the-fly. To achieve this,
locations (as well as the current environment, heap, stack, and we extend our symbolic configurations with two additional
φ nodes) and merges the subgraphs rooted at those locations. data structures: a function φ : PC 7→ ℘(Stack ) mapping
The algorithm visits the subgraphs in lockstep in a breadth-first program counter indexes to sets of stacks to record the
search (BFS) fashion and joins every location (node) with the symbolic stacks at the merge points of control flow branches,
same field/variable label. This is achieved by creating a fresh and a set of program counter indexes ψ ⊆ ℘(PC ) to record
location and updating references to the new location. If the two backward jumps associated with loops. The former is similar
merged locations have fields/variables with the same name, to the standard φ-node is high-level languages and we use
it recursively applies the update function. Observe that the it to merge the stacks corresponding to different branches in
update modifies the state of the symbolic computation and may the CFG. We assume that all stacks at a merge point have
affect different components of the configuration. This approach the equal size, which is ensured by the high-level language
is flow-insensitive as it updates symbolic configurations with compiler (e.g., the C# compiler) that translates source code
new symbolic values, instead of overwriting the old values of to CIL code. The set ψ ensures that loops are not analyzed
the variables/fields. repeatedly. Since our analysis is flow- and path-insensitive,
it suffices to analyze each basic block only once. Figure 5
1: arg.obj = new ClassB();
illustrates our algorithm for control flow instructions. We use
2: arg.next = new ClassA(); 4a: ldvar arg //S-LdVar
a function mergeStacks : ℘(Stack ) × Heap × Env × Φ 7→
3: arg.next.obj = new ClassB(); 4b: ldfld next //S-LdFld Stack × Heap × Env × Φ to merge all stacks and update
4: arg = arg.next; 4c: stvar arg //S-StVar the new symbolic configuration. Specifically, mergeStacks
merges symbolic locations pointwise, and updates the pointers
Listing 6: Merging heap locations to the merged locations in the other components.
The code snippet in Listing 6 illustrates our symbolic We describe the few interesting rules in Figure 5 via
analysis of the heap. Our abstract interpretation yields the heap examples. Consider the CIL representation of the C# ternary
graph in Figure 4a after analyzing the (CIL representation of) operator in Listing 7, which assigns the location in var1 or
instructions (1-3) in Listing 6. We now illustrate our analysis var2 to arg.obj depending on the truth value of f lag. The
for instruction (4) and its CIL representation (4a-4c). We first analysis should compute that field arg.obj points to the merged
load the symbolic locations in variable arg and field next location of variables var1 and var2. Observe that such case is
onto the symbolic stack by applying rules S-L DVAR and S- not handled by the update function in Figure 3. Our analysis
L D F LD, respectively. This results in loading the location lb in merges the locations in var1 and var2 on the stack using rule
Figure 4a. Next, we apply rule S-S T VAR for instruction (4c). S-S T U PD. This rule has higher precedence over any other rule.
The rule considers the subgraphs rooted at location lb (the top Initially, φ(pc) = ∅ for all program points. For every forward
element of the stack) and at the location la (since E(arg) = la ) jump, as in rules S-B R F WD and S-B RT RUE F WD, we store
and applies the update function. Since both edges originating the current stack for the target instruction. For instance, the
from the locations la and lb are labeled with the field obj instruction at index (5), i.e., br 7, stores the symbolic stack
(which contain the locations lc and ld ), the algorithm merges containing the locations in arg and var2 for φ(7). When
these locations to a fresh location lcd and updates the graph analyzing the instruction stfld obj at index (7), the analyzer
as shown in Figure 4b. first applies rule S-S T U PD to merge the stack stored in φ(7)
and the current stack, which contains the locations in arg
Abstracting the control flow. The main challenge to and var1. Then, rule S-S T F LD ensures that the field arg.obj
analyzing control flow instructions is the lack of structure contains the merged location.
7
S-S T U PD S-S T S KIP
φ(pc)↓ (E 0 , h0 , s0 , φ0 ) = mergeStacks(φ(pc) ∪ {s}, E, h, φ) s=⊥
hpc, E, h, s, φ, ψi → hpc, E 0 , h0 , s0 , φ0 [pc 7→ ⊥], ψi hpc, E, h, s, φ, ψi → hpc + 1, E, h, s, φ, ψi
S-B R F WD S-B RT RUE F WD
P (pc) = br i i > pc φ0 = φ[i 7→ φ(i) ∪ {s}] P (pc) = brtrue i i > pc φ0 = φ[i 7→ φ(i) ∪ {s}]
hpc, E, h, s, φ, ψi → hpc + 1, E, h, ⊥, φ0 , ψi hpc, E, h, s :: sv, φ, ψi → hpc + 1, E, h, s, φ0 , ψi
S-B R B WD
P (pc) = br i i < pc φ0 = (pc ∈ ψ ? φ : φ[pc 7→ s]) (pc0 , s0 , ψ 0 ) = (pc ∈ ψ ? (pc + 1, ⊥, ψ) : (i, s, ψ ∪ {pc}))
hpc, E, h, s, φ, ψi → hpc0 , E, h, s0 , φ, ψ 0 i
S-B RT RUE B WD
P (pc) = brtrue i i < pc φ0 = (pc ∈ ψ ? φ : φ[pc 7→ s]) (pc0 , ψ 0 ) = (pc ∈ ψ ? (pc + 1, ψ) : (i, ψ ∪ {pc}))
hpc, E, h, s :: sv, φ, ψi → hpc0 , E, h, s, φ, ψ 0 i
Fig. 5: Abstract interpretation of control flow
undefined stack using rule S-S T U PD, and uses the new stack,
// arg.obj = flag ? var1 : var2; while updating the φ node. Subsequently, the analyzer loads
1: ldvar arg // S-LdVar the variable f lag onto the stack and examines the instruction
2: ldvar flag // S-LdVar brtrue 2 at index (16) via rule S-B RT RUE B WD. Since 16 6∈ ψ,
3: brtrue 6 // S-BrTrueFwd
this results in transferring control to the instruction at index
(2) and analyzing the loop body. If the analyzer reaches the
4: ldvar var2 // S-Ldvar
instruction brtrue 2 again, it finds that the instruction has
5: br 7 // S-BrFwd already been analyzed, i.e., 16 ∈ ψ, and continues with the
6: ldvar var1 // S-StUpd and S-LdVar next instruction.
7: stfld obj // S-StUpd and S-StFld
1: br 15 // S-BrFwd
Listing 7: Ternary operator in CIL 2:
//loop body
While the previous rules ensure the consistency of the 15: // S-StUpd
stack, we should also cater for potential loops resulting from // while (flag)
backward jump instructions. Thanks to our flow-insensitive ldvar flag // S-LdVar
analysis, it suffices to analyze the "loop body" only once. 16: brtrue 2 // 2 x S-BrTrueBwd
Specifically, we use a set ψ to keep track of the control flow
instructions that trigger a backward jump and ensure that the Listing 8: While loops in CIL
instructions at the jump target is analyzed only once (see
S-B R B WD and S-B RT RUE B WD). In particular, whenever an Aliasing and taint tracking Recall that the goal of our
unconditional jump has already been analyzed, i.e. pc ∈ ψ, analyses is tracking information flows from sensitive sinks to
we set the stack to ⊥ (undefined) and move on to executing attack triggers. To achieve this, we enrich the location nodes in
the next instruction. An undefined stack will simply skip the our abstract heap graph with a taint mark whenever the return
analyzes of the current instruction as in rule S-S T S KIP unless value of a sensitive sink is analyzed. Thanks to our store-
there was another jump to that instruction with a defined stack based abstract heap model, the heap graph already accounts
(in which case rule S-S T U PD applies)1 . for aliases to a given node. In fact, aliases can be computed
by looking at the labels of incoming edges to a given location
We illustrate our analysis of backward jumps with the node. Therefore, we can compute the taint mark of a reference
example in Listing 8. The example models the CIL represen- by reading the taint mark of the node it points to.
tation of the C# pattern while(flag) {loop body}. The
analyzer examines the instruction br 15 at index (1) via rule S- Figure 6 provides an example of aliasing and taint tracking.
B R F WD, recording the current stack for the instruction at index The call to the sensitive sink at line (1) pushes the return
(15) in φ and updating the stack to undefined. This is because value onto the stack, marks the corresponding node as tainted
at this point we do not know if the next instruction at index and adds an edge labeled with b.f oo to the tainted node.
(2) will be reached from another configuration. Therefore, we Similarly, the instruction at line (2) creates an alias of b.bar
simply skip the following instructions (rule S-S T S KIP) until to the tainted node, which yields the heap graph in Figure 6b.
we reach a merge point, i.e., an instruction where φ(pc) is Finally, the analysis of the virtual call at line (3) reveals that
defined. In our example, the merge point is the instruction at the caller b.bar is tainted, hence an attacker controlling its type
index (15). The analyzer merges the stack in φ(15) with the determines which concrete implementation of V irtualCall()
is executed. Therefore, we consider such method as a potential
1 We assume that φ(pc) ∪ ⊥ = φ(pc) attack trigger.
8
S-C ALL K
arg b P (pc) = call pc0 K(sigpc0 )↓ σ 0 = apply(K(sigpc0 ), σ)
1: b.foo = SSink(arg);
2: b.bar = b.foo; hpc, cs, σ, Ki → hpc + 1, cs, σ 0 , Ki
la lb foo T
3: b.bar.VirtualCall();
S-C ALL
bar P (pc) = call pc0 K(sigpc0 )↑
(a) Code (b) Heap hpc, cs, σ, Ki → hpc0 , cs :: (σ, pc), ⊥, Ki
Fig. 6: Aliasing and taint tracking S-C ALL E XT
P (pc) = call pc0 P (pc0 )↑ l ∈ Loc fresh
hpc, cs, h_, _, s, _i_, Ki → hpc + 1, cs, h_, _, s :: l, _, _i, Ki
C. Modular inter-procedural analysis S-E ND
sum = cmptSum(σ) σ 00 = apply(sum, σ 0 ) P (pc)↑
We now present the inter-procedural symbolic analysis hpc, cs :: (σ , pc ), σ, Ki → hpc0 + 1, cs, σ 00 , K[sig 7→ sum]i
0 0
underpinning our computation of OIV patterns. The analysis
relies on a preliminary stage that reconstructs the Call Graph
Fig. 7: Abstract interpretation of call graph
containing the entry points that may reach sensitive sinks.
Subsequently, it performs a modular analysis of the call graph,
based on method summaries, to determine OIV patterns.
Call graph analysis. We first analyze the target set of (c) calls to (non-recursive) methods with no summaries in the
CIL assemblies to identify method signatures associated with cache K (rule S-C ALL) ; and (d) updates of the cache K upon
call and callvirt instructions, and store them as keys in a termination of the analysis of a method (rule S-E ND).
hash table with the caller methods as values. The hash table Rule S-C ALL K applies the cached summary of the method
represents a call graph, which we can reconstruct via backward with signature sigpc0 (at index pc0 ) to the current symbolic
analysis. A path from a sensitive sink to an entry point can state σ of the caller, using a function apply : Sum × State 7→
be computed in O(n) time, where n is the call stack’s depth. State. In a nutshell, apply takes the root variables Var of
We also compute the type-hierarchy graph to determine all the summary consisting of the formal parameter arg and a
implementations of virtual method calls. We assume that a predefined variable rv ∈ Var storing the return value of the
virtual call of a base method can transfer control to any method. Then, it pops off the top value from the stack in σ and
implementation of that method and store such information in merges it with arg using the function update described in Sec-
the call graph. The analyzer uses this information during the tion IV-B. The merging process may affect other components
backward reconstruction of the call graph from a sensitive sink of σ that contain references to merged locations, resulting in
to entry points, as well as during the abstract interpretation of an updated state σ 0 . Rule S-C ALL E XT handles external/native
callvirt instructions. method calls by pushing a fresh symbolic location onto the
Inter-procedural analysis with method summaries. We stack whenever a method lacks implementation, i.e., P (pc0 )↑.
perform a modular dataflow analysis for every entry point Rule S-C ALL triggers the intra-procedural analysis of a new
identified in the preliminary stage. Whenever our algorithm method by transferring control to its code at index pc0 and
reaches a new method, it triggers the intra-procedural analysis storing the context of the caller in the symbolic stack cs. The
(described in Section IV-B) to analyze the method inde- caller’s context contains the caller’s state and program counter
pendently of the caller’s context, i.e., both the heap h and index pc. Observe that the analysis of the callee method is
the environment E are empty. As a result, it produces a performed in a context independent manner, i.e, σ 0 = ⊥.
compact representation of the heap graph called summary. The Rule S-C ALL matches rule S-E ND to compute the summary
summary is then stored into a caching structure K, and it is upon termination of the method’s intra-procedural analysis
reused for every subsequent call to the same method. (denoted by P (pc)↑). Subsequently, it applies the summary
to the caller’s context σ 0 and caches it in K, and continues the
We use the following notation to describe the abstract analysis with the caller’s next instruction at index pc0 + 1.
interpretation of method calls: A state σ ∈ State is a tuple
(E, h, s, φ, ψ) representing the calling context in a symbolic Example: Method calls. We illustrate the abstract inter-
configuration and it is stored whenever we start the analysis of pretation of non-recursive calls in Listing 8. The analysis starts
a new method. The symbolic call stack cs ∈ (State × PC )∗ from the method EPoint and calls SSink which is an external
is a stack of pairs (σ, pc) containing the state of the caller method, hence P (pc0 )↑. Rule S-C ALL E XT allocates a fresh
and program counter index of the caller in state σ. A partial location and pushes it onto the stack to emulate the return
mapping K : Sig 7→ Sum caches method summaries for each value. Because the method signature is defined as sensitive
method signature. A method summary sum ∈ Sum is defined sink, we mark the fresh variable as tainted. Subsequently, the
by the tuple (E, h) consisting of the environment and the heap. assignment stores the tainted value to the location in b.f oo.
Figure 7 presents the algorithm for our summary-based Next, we call the method CreateAlias which triggers an
inter-procedural analysis of a call graph. We handle the follow- intra-procedural analysis of its body via rule S-C ALL after
ing cases: (a) calls to methods with summaries already present storing the current σ and pc to the call stack. The analysis
in the cache K (rule S-C ALL K); (b) calls to external/native applies rule S-S T F LD to create an alias between arg.bar and
method with no implementation available (rule S-C ALL E XT); arg.f oo. Finally, rule S-E ND builds a summary from the
9
void EPoint(ClassA arg) { of arbitrary type. SerialDetector analyzes only CIL code in
var b = new ClassB(); .NET assemblies and does not support binary code as in native
b.foo = SSink(arg); methods. Therefore, we take a conservative approach that every
CreateAlias(b);
native method returns an object of any derived type as the
b
return type. We then mark the return object of the sensitive
Foo(b.bar);
sink as tainted. The attack trigger is described as either a native
} lb foo T (external) method that takes a tainted object as parameter or a
void CreateAlias(ClassB arg){ bar virtual method with the first argument marked as tainted.
arg.bar = arg.foo;
(b) Heap The pipeline of the detection phase consists of four steps:
}
(1) SerialDetector builds an index of method call’s graph
void Foo(ClassB arg) { for the whole .NET assembly dataset; (2) It filters all native
ExternalMethod(arg); method signatures using the criteria defining the sensitive
} sinks. This step yields the signatures of sensitive sinks, which
we use to build the slices of the call graph in the backward
direction, from the sensitive sinks to entry point methods;
(a) Code (3) SerialDetector performs a summary-based inter-procedural
Fig. 8: Method calls dataflow analysis as described in Section IV; (4) It outputs a
sequence of patterns containing calls to attack triggers for each
sensitive sink as well as traces from entry points to sensitive
sinks. We collect these patterns in a knowledge base and use
current symbolic state and stores it in the cache. The summary
them as input to the exploitation phase.
generation algorithm traverses the heap graph h starting from
root variables V ar in E and stores visited nodes and references Exploit generation and validation. Drawing on the
to the summary. This is the only information that may affect knowledge base from the previous stage, we manually identify
the context of the caller. Subsequently, the algorithm applies usages of vulnerable patterns in frameworks and libraries. To
the summary to the caller’s state to create a new state that this end, we leverage the YSoSerial.Net project [3] to create
accounts for the effects of the method call, and proceeds with templates that can be used to exploit vulnerabilities in a target
executing the next instruction of the method EPoint. Figure 8b application. We do this by declaring a signature of each public
depicts the effects of the summary applications, which add the vulnerable method directly in C# code using DSL-like API.
edge labeled with bar to the heap graph, thus causing the two Listing 9 shows the template for the vulnerable YamlDotNet
fields to point to the tainted node. library from Section II-B.
Finally, we analyze method Foo via rule S-C ALL. Foo var deserializer = new Deserializer();
contains an external method call (as analyzed by rule S-
Template.AssemblyVersionOlderThan(5, 0)
C ALL E XT) with argument arg as parameter. Since external
.CreateBySignature(it =>
methods can be used as attack trigger, we store information
about the ExternalMethod in the node of the arg location. deserializer.Deserialize(
The rule S-E ND builds and stores the summary, and applies it it.IsPayloadFrom("payload.yaml").Cast(),
to the EPoint context when reaching the end of the method. typeof(object)));
Hence, we merge two locations (b.bar which is passed to Foo,
and arg from the summary), and detect the call to an attack Listing 9: Object Injection Template
trigger with a taint mark. Finally, we store the chain from
EPoint to SSink and ExternalMethod as an OIV pattern. We designed a DSL as custom LINQ expressions. LINQ
is a uniform programming model for managing data in C#.
V. I MPLEMENTATION Each method in the DSL call sequence refines the template
model. For example, we start with the Template static
This section provides implementation details and limita-
class and call the method AssemblyVersionOlderThan to
tions of SerialDetector. Figure 2 overviews the architecture.
specify a vulnerable version of the library. The next method
call CreateBySignature creates a template for the method
A. Anatomy of SerialDetector Deserialize of the YamlDotNet serializer and defines
SerialDetector [41] is written in C# and runs on the .NET as the first parameter the untrusted input with a payload
platform using the dnlib library [1] for parsing assemblies. from payload.yaml. The DSL facilitates the description
of payloads and it allows to apply one payload to many
Pattern detection. The distinguishing feature of SerialDe- templates. The key feature of the DSL is usage the expression
tector is that it implements the framework-agnostic paradigm tree as parameter to the method CreateBySignature. The
and does not use any heuristics based on method or class names expression tree represents code in an abstract syntax tree
to detect OIV patterns. The input consists of a set of .NET (AST), where each node is an expression. The method can
assemblies and rules for sensitive sinks and attack triggers. extract a signature of the calling method from the expression
The sensitive sinks are initially described as a native method tree, e.g., deserializer.Deserialize, to detect any usage
that return an object of type System.Object. Thereby, we in a target application. Moreover, it can also compile and
assume that an attacker can manipulate either the parameter run the expression tree code to test the payload. A main
of the sensitive sink or the runtime state to get an object advantage of template generation with our DSL is that it
10
facilitates modification and testing of different payloads, which of the method and apply all generated summaries. To reuse
is essential during exploitation, when SerialDetector sends merged summaries of all virtual method implementations,
a signal upon successful execution of a malicious action. we introduce fake methods that include concrete calls of all
SerialDetector comprises following steps for exploit generation implementations of a certain virtual method. We cache the
and validation: summary of such method for future use.
1. Matching (Manual): To validate the results of the detection We implement a lightweight form of context-sensitive
phase, we match the generated patterns with actual sensitive analysis. The analyzer collects types of all created objects in a
sinks and attack triggers of an exploit with a known gadget. global context and then resolves the virtual method calls only
We generate a payload for the known gadgets and reproduce for the implementations of the collected types. Because we use
the exploit of each target serializer. We attach a debugger the modular approach we need to track summaries that have
to our reproduced case and set breakpoints to the detected virtual calls. When a new type is instantiated, we invalidate
sensitive sink and attack trigger calls. If the breakpoints are the summaries that have the virtual calls that may be resolved
triggered and the attack trigger performs a call chain to the to methods of the new type.
malicious action of our payload, then we conclude that the Some virtual methods of .NET Framework have hundreds
pattern is exploitable. of implementations. Thereby, the analysis of all implementa-
2. Populating Knowledge Base (Manual): We use the results tions is a very expensive operation that often does not give us
of the matching to populate a knowledge base. We describe benefits. We implement several optimizations for virtual calls.
the code of a gadget to create and transform to various Whenever possible, the analyzer infers the type of virtual calls
formats to generate the payload. We also describe signatures in the intra-procedural analysis. Thereby, we can reduce the
of vulnerable entry points from the matched patterns in number of implementations for data flow analysis. Otherwise,
templates as well as additional restrictions, e.g., the version we limit a count of implementations of virtual methods calls
of a vulnerable library. for data flow analysis and track all cases where the analyzer
3. Payload and Template generation (Automated). SerialDetec- skips the implementations. We then perform a manual analysis
tor automatically generates payloads and templates based on of such cases and pick the ones of interest for the next run of
described knowledge base rules. the analysis.
4. Call Graph Analysis (Automated). We use the templates
as input for Call Graph Analysis to detect potentially Recursion. Another challenge is the modular analysis of
vulnerable templates in a target application. SerialDetector recursion calls. The analysis must ignore caching summaries
generates the Call Graph from the application entry points of intermediate methods in a chain of recursive methods.
to the vulnerable calls described in the templates. The reason for this is that the summaries of intermediate
5. Template validation (Automated). SerialDetector automati- methods do not contain full data-flow information until we
cally generates and run tests for templates. It validates that complete the analysis of the first recursive method. However,
a given payload can exploit an entry point in the templates. a program may have many calls of the same intermediate
It also validates Call Graph Analysis step using template method, hence we must reanalyze such method, although we
description as a source for compiling the .NET assembly get the same incomplete summary. We use temporary caches
with vulnerable code and it runs the analysis against this for the summaries of intermediate recursive methods to analyze
sample. All information required for testing is extracted such methods only once within a recursion call. We then
from the knowledge base. invalidate the temporary cache when the analysis of the first
6. Exploit Generation (Manual). SerialDetector relies on the recursive method is completed.
human-in-the-loop model for exploit generation. It provides Static fields. The CLI specification allows types to declare
an automatically generated call graph targeting a vulnerable locations that are associated with those types. Such locations
template and an input payload that exploits the template. A correspond to static fields of the type, hence any method has
security analyst explores the entry points of the call graph access to the static fields and can change their value. While our
subject to attacker-controlled data, and exploits them using abstract semantics does not address static field, SerialDetector
the original payload. The analyst may need to combine OIVs does. We enrich the summaries with an additional root variable
with other vulnerabilities (e.g., XSS - see Section VII-C) storing the names of types with static fields. Thus, we can
to execute a malicious payload for a target entry point. access any location of the static field by using such variable
If an exploit fails, the analyst investigates the root cause and the full access path. Then, we merge such root variable
using other tools (e.g., a debugger) and modifies the payload as we do with other arguments of the method when applying
according to application-specific requirements. a summary to the calling method’s context.
Arrays. The CLI specification defines a special type for
B. Challenges and Limitations arrays, providing direct support in CIL (newarr, stelem,
ldelem, and ldelema). Array instructions may perform integer
Virtual method calls. Static analysis for large code is very
arithmetics when accessing an array element by taking its array
challenging. We find that modularity and flow insensitivity are
index from the evaluation stack. We do not support integer
essential for analyzing millions of LOC. One of the challenges
arithmetics for primitive types in the current version of the
we faced was the analysis of virtual method calls. When per-
analyzer. Thereby, we overapproximate the array semantics
forming a call graph analysis, we assume that a virtual method
by assuming that all elements of an array point to the single
call may transfer control to a method of any instantiated type
abstract location containing all possible values.
that implements this virtual method. For a modular data flow
analysis, this means that we must analyze all implementations Unsupported instructions. The CLI specification supports
11
method pointers and delegates [2]. A method pointer is a type Table I presents the results of our experiments. We report
for variables that store the address of the entry point to a the Version of the library or the framework containing that
method. A method can be called by using a method pointer library, and the number of different Methods analyzed for
with the calli instruction. Delegates are the object-oriented each entry point. The analyzer generates a summary for each
equivalent of function pointers. Unlike function pointers, dele- method. We need re-analyze some methods, for example,
gates are object-oriented, type-safe, and secure. Each delegate recursive methods or methods with virtual calls that must be re-
type provides a method named Invoke with appropriate pa- analyzed after creating an instance of the type with a concrete
rameters, and each instance of a delegate forwards calls to its implementation. Therefore, the number of summaries is always
Invoke method to one or more static or instance methods on greater than the analyzed methods.
particular objects. SerialDetector does not track values for the
delegates and the method pointers, however it issues a warning The column Patterns shows the number of unique OIV
whenever such features are used. patterns for each serializer, while Priority Patterns shows
patterns that contain the methods of known gadgets. The
Both CLI and the .NET Framework support reflection. pattern consists of the attack triggers that are called on a
Reflection provides the ability to examine the structure of unique tainted object. It is unclear whether or not the rest of
types, create instances of types, and invoke methods on types, attack triggers is exploitable, since this requires detection of
all based on a description of the type. The current version new gadgets, which we do not address in this work. Therefore,
of the analyzer does not reconstruct the call graph based on the number of (priority) patterns minus one corresponds to the
information of method invocations via the reflection. number of (gadget specific) false positives.
Exploitation phase. We carry out an in-breadth analysis
VI. E VALUATION of .NET applications vulnerable to OIVs using the follow-
This section presents our experiments to validate the effi- ing methodology: (1) We collected vulnerabilities from the
ciency and effectiveness of SerialDetector. We leverage known National Vulnerability Database using the keyword ".NET"
vulnerabilities in the .NET Framework and third-party libraries and category "CWE-502 Deserialization of Untrusted Data"
as ground truth for checking the soundness and permissiveness as of January 1st, 2019. As a result, we obtained 55 matched
of the detection phase, as well as for evaluating the scalability records; (2) We inspected the vulnerabilities manually and
of analysis on a large codebase. To evaluate the exploitation found that 11 vulnerabilities were actually detected in .NET
phase, we perform an in-breadth study of deserialization vul- applications, of which only 5 vulnerable applications were
nerabilities on real-world applications over the past two years, available for download; (3) We analyzed these applications
and report of the effort to exploit these vulnerabilities with with SerialDetector as reported in the first part of Table II;
SerialDetector. We perform the experiments on an Intel Core (4) Since not all vulnerabilities of insecure deserialization are
i7-8850H CPU 2.60GHz, 16 GB of memory, running Windows marked as CWE-502, we searched the Internet for additional
OS and .NET Framework 4.8.04084. The analysis results and OIVs and added them in our experiments, including the new
data are available in SerialDetector’s repository [41]. vulnerabilities that we found in Azure DevOps Server. In total,
we run SerialDetector against 7 different applications with
First, SerialDetector indexes all code of the .NET Frame- 10 OIVs. SerialDetector detected vulnerable calls of insecure
work and detects the list of sensitive sinks. The .NET deserializers and related entry points in all applications except
Framework consists of 269 managed assemblies with 466,218 for the Telerik UI product, which uses the Reflection API
methods and 50,399 types. SerialDetector completes this step to call an insecure configuration of JavaScriptSerializer. The
in 12.4 seconds and detects 123 different sensitive sinks. current version of SerialDetector does not support reflection
Not all sensitive sinks create new objects dynamically based for reconstructing the call graph and ignores such calls.
on input data, hence we filter out such sensitive sinks
after manual analyisis. For example, the external method Table II contains information about the number of as-
Interlocked.CompareExchange is considered as sensitive semblies and analyzed instructions to illustrate the size of
sink, however it only implements atomic operations like com- applications. The column "Entry Points w/o Threat Model"
paring two objects, hence we exclude it from our list. provides information about the count of all detected entry
points that reach insecure serializer calls. However, not all
Detection phase. To evaluate true positives, false positives, assembly entry points are available for attackers to execute.
and false negatives of the detection phase, we run SerialDetec- Some are never called by an application, while others require
tor against known OIVs in .NET Framework and third-party privileges that are inaccessible to the attacker. The exploitable
libraries using insecure serializers from the YSoSerial.Net entry points depend on the threat model which is specific to
project [3]. We use the deserialization methods of insecure se- an application. We describe the possible threat models for a
rializers as entry points for our data flow analysis. The analyzer web application in Section VII-B. To provide an assessment
generates OIV patterns for each deserializer. We then match the in line with the actual operation mode of SerialDetector, we
attack triggers with gadgets from YSoSerial.Net as an indicator leverage the (known) vulnerable entry points and compute the
of effectiveness. SerialDetector confirmed exploitable patterns number of detected entry points for a specific threat model.
for 10 deserializers. It also reported warning for 5 deserializers Thus, an attacker first identifies the parts of the target system
DataContractJsonSerializer, DataContractSerializer, FsPickler, (assemblies) that are reachable for a threat model and then
NetDataContractSerializer, and XmlSerializer since it lacks runs a detailed analysis. The column "Entry Points w/ Threat
support for delegates calls. If a code snippet uses a delegate Model" reports the results of SerialDetector. The total number
to create a type, we lose information about that type, hence of entry points estimates the upper bound (it also includes true
SerialDetector cannot resolve virtual calls of that type. positives) on the number of false positives of our analysis.
12
Time Memory Priority Method Applied
Version Patterns Methods Summaries Instructions
(sec) (Mb) Patterns Calls Summaries
BinaryFormatter .NET 4.8.04084 1.5 7,208 6 6 5,263 6,342 31,600 29,094 214,784
DataContractJsonSerializer .NET 4.8.04084 122.2 16,042 73 - 14,091 16,230 112,322 102,079 576,896
DataContractSerializer .NET 4.8.04084 51.9 13,942 73 - 13,631 15,748 109,179 99,294 562,410
FastJSON 2.3.2 3.3 7,495 24 15 6,564 7,701 41,615 37,740 273,806
FsPickler 4.6 1.5 7,216 7 - 3,552 4,302 22,927 20,362 152,343
JavaScriptSerializer .NET 4.8.04084 44.9 13,234 121 9 18,616 19,727 130,426 120,007 665,524
LosFormatter .NET 4.8.04084 86.3 15,278 9 9 18,941 21,631 146,864 135,843 773,037
NetDataContractSerializer .NET 4.8.04084 158.2 17,578 72 - 14,021 15,613 104,941 96,216 545,699
Newtonsoft.Json 12.0.3 7.6 7,776 13 10 12,560 14,373 90,385 84,208 496,888
ObjectStateFormatter .NET 4.8.04084 2.5 7,213 9 9 6,287 8,407 47,756 43,495 314,952
SharpSerializer 3.0.1 47.9 13,180 69 2 12,819 14,340 94,317 87,830 500,922
SoapFormatter .NET 4.8.04084 8.0 7,743 12 12 11,552 12,786 79,603 73,698 444,448
XamlReader .NET 4.8.04084 10.4 7,754 133 23 14,627 17,209 109,160 101,921 594,230
XmlSerializer .NET 4.8.04084 158.2 16,766 82 - 14,511 16,022 114,808 106,728 583,887
YamlDotNet 4.3.1 6.0 7,754 44 2 7,253 8,441 54,581 51,080 300,192
TABLE I: Evaluation results for the insecure serializers
Entry Points Entry Points
Assemblies/ Payload
Software Version Serializer w/ Threat Model w/o Threat Model
Instructions Changes
(False Positives UB) (False Positives UB)
84/
CVE-2020-14030 Ozeki SMS Gateway 4.17.6 BinaryFormatter 31 220 0
1,866,312
CVE-2020-10915 10/
VEEAM One Agent 10.0.0.750 BinaryFormatter 29 29 1
CVE-2020-10914 199,185
Telerik UI
CVE-2019-18935 2019.2.514 JavaScriptSerializer - - - -
for ASP.NET AJAX
191/
CVE-2019-10068 Kentico 12.0.0 SoapFormatter 1 1 0
5,647,128
4/
CVE-2019-19470 TinyWall 2.1.8 BinaryFormatter 4 30 0
39,927
6,283
Microsoft SharePoint 16.0. Microsoft.SharePoint.dll; 55/
CVE-2019-0604 XmlSerializer 49,007 2
Server 2019 10337.12109 9 8,329,428
Microsoft.SharePoint.Portal.dll
Azure DevOps 17.143. 326/
CVE-2019-1306 BinaryFormatter 14 20 2
Server 2019 28621.4 10,742,006
CVE-2019-0866 Azure DevOps 370/
RC2 YamlDotNet 3 13 1
CVE-2019-0872 Server 2019 9,863,890
TABLE II: Evaluation results for the real-world applications
CVE-2019-0604 in SharePoint Server has two ex- the analysis of virtual calls in a conservative way, analyzing
ploitable entry points in different assemblies [49]. SerialDe- all implementations of a virtual method and applying the
tector finds that both entry points and many others reach summaries at call site. This approach generated correct patterns
XmlSerializer::Deserialize call. An outlier is Mi- for very few serializers (e.g., BinaryFormatter), but it did
crosoft.SharePoint.dll with 6,283 detected entry points. The not terminate for many others. The implementation of the
main cause of such high complexity is the tight coupling type-sensitive analysis improved performance for all tested
of code in SharePoint Server and its main assembly Mi- serializers.
crosoft.SharePoint.dll, as well as our over-approximation of
virtual calls. For each vulnerable entry point, we followed False Positives. We also find attack triggers that are never
the approach described in Section V to generate and validate called for a tainted object. The root cause for these false
the exploits. In our experiments, we changed the payload as positives is flow-insensitivity of the data flow analysis. The
reported in Table II. We further clarify the practical details of flow-insensitive approach allows us to control the heap size at
threat models and exploit changes in Section VII-A. the expense of the precision of analysis. On the other hand,
our results show that the number of patterns that should be
Performance. The analysis is quite fast for such a large reviewed manually by a security analyst is not overwhelming.
project as the .NET Framework. The average time of the
analysis for a single serializer is 47.4 sec. This shows the ad-
vantages of our modular inter-procedural analysis. We also ex- VII. I N - DEPTH A NALYSIS OF A ZURE D EVO PS S ERVER
perimented with a whole-program dataflow analysis algorithm
which did not terminate within a limit of hours. Our flow- We evaluate SerialDetector on production software to val-
insensitive approach reduces the size of the heap graph. This idate its usefulness in practical scenarios. We choose Azure
enables SerialDetector to apply summaries and merge locations DevOps Server as the main target for our investigations
faster, thus improving the overall analysis time. Another factor mainly due to its complexity and diversity of threat models.
improving scalability is the usage of the lightweight context- Section VII-A provides a brief summary of Azure DevOps
sensitive analysis. Earlier versions of SerialDetector performed and Section VII-B provides a thorough overview of the threat
13
DMZ Network Internal Network
DMZ Network Internal Network
OS Account
OS Account
OIV 2a Database
Database
User 1 Web App
Web App
2b OIV
User 3b Internal
Internal 1b V Service
2c Service
Agent 2a
Local Service
Files
OS Restricted
Account
Shared
Files
Fig. 9: First threat model
1a
2b
models that we explored. Section VII-C describes process of
using SerialDetector to discover unknown vulnerabilities. Fig. 10: Second threat model
A. Microsoft Azure DevOps
The Azure DevOps Server (ADS) is a Microsoft product
that provides version control, reporting, requirements manage- an attacker and send payloads to the web application. If the
ment, project management, automated builds, lab management, application has an entry point that receives user data and
testing, and release management capabilities. These features subsequently uses code that is subject to OIVs, we can access
require integration with various data formats and serializers, any resources available to the OS account. This is depicted by
thus making it an excellent target for finding OIVs. ADS OS Account trust boundaries in Figure 9. The attacker may
hosts multiple projects across different organizations. Projects send a payload to a vulnerable application directly (arrow
are grouped into isolated collections and the system provides 1) and get access to local files (arrow 2c), services into the
functionalities to set up a project and its collections, as internal network (arrows 2a, 2b) or any data from the web
well as to manage users in a flexible manner. Thereby, a application memory. Example 1 illustrates this scenario.
vulnerability that exposes high privileges in one project may Our second threat model addresses the question: Can an
lead to information disclosure of another project. ADS stores OIV be exploited if it processes data from internal services or
confidential information that is intellectual property (e.g., the files only? The answer depends on other components of the
source code of products), hence a disclosure has high impact. system. Figure 10 presents the threat model for such cases.
ADS consists of many services exchanging information An attacker may already be inside DMZ network and execute
with each other, for example, the main web app, crawler and code with very restricted privileges. For example, the attacker’s
indexer services. Such system design implies complex threat process may have access only to the shared files originating
models in which even internal data can be untrusted. The server from the web application. If these files are processed by code
has many entry points such as request handlers, documented subject to OIVs, the attacker can transfer the payload through
REST APIs, plugin APIs, and internal and undocumented API. files (arrow 1a), escalate privileges to the web application
After analyzing different threat models, we use SerialDetector account (arrow 2a), and ultimately gain access to all resources
to automatically determine attacker-controlled entry points inside the OS Account area in Figure 10.
leading to OIVs. We then scrutinize these entry points to find
Another scenario includes remote attacks through chains
RCE exploits using automated and manual analysis.
of vulnerabilities in other services. A service that receives
untrusted user data may have vulnerabilities such as Server-
B. Threat models Side Request Forgery (SSRF) enabling an attacker to deceive
We first consider the simple threat model of a web applica- the server-side application to make requests to an arbitrary
tion running under an OS account. ADS uses the NETWORK server, including internal servers. A service may also have
SERVICE account in Windows by default. The code executing insufficient data validation and allow to store a payload to an
in the web application process has restrictions according to internal service that subsequently makes this data available to
the OS account permissions. The web application usually has code vulnerable to OIVs. For example, an attacker may abuse
access to different services into the internal network, e.g., a data validation vulnerability in the Agent service (arrow 1b)
indexing or caching services that handle the application data. and send the payload to the Internal Service (arrow 2b). The
The application may also have access to a database with Internal Service may index the data and send the payload to
OS account permissions or specific credentials. Thereby, any an application with OIVs (arrow 3b). As a result, the attacker
code that executes into the web application process may have will gain access to all resources inside the OS Account area.
access to the database. Users communicate only with the web
Our third threat model (Figure 11) targets scenarios where
application in the demilitarized zone (DMZ) and do not have
only a user with administrator privileges can get access to
access to the internal network.
code subject to OIVs. ADS exposes web applications with a
Figure 9 illustrates the expected information flows between rich user management subsystem enabling the owner to create
services and users via black arrows. Any user can act as isolated projects with their own administrator accounts. We
14
DMZ Network
instantiate objects for a malicious gadget and execute a pay-
OS Account
load. However, the BinaryFormatter handles data from local
Web App Database
storage which an attacker cannot control directly. Following
Admin Project A
the threat model in Figure 10, SerialDetector found that one
3b OIV
Admin A Admin
4b
Organization A of the methods that call BinaryFormatter is located in the
Features Data
code of the Search Engine. The Search Engine computes
2b
indexes of text data like source files and Wiki pages to enable
1b
XSS quick search of information. This service is a part of Web
User A User User
Features Data App in the threat model and is not accessible from outside.
2a The indexes represent binary formatted data managed by the
Storage Service. The Storage Service allows to get indexes
Admin Project B from other components of the system and makes them available
1a OIV to the Search Engine. This corresponds to Internal Service in
Admin B Admin Organization B
Features Data the threat model. A separate service Crawler tracks changes
in the Git repository, parses the changed text files according
to their format, and sends the resulting data to the Storage
User B User User Service. The data in the Git repository is untrusted because
Features Data
users with minimal privileges usually have access to some
repositories. This user-controlled data corresponds to User
node in Figure 10. Hence, the security of the system relies on
proper validation of the data from Git to the Crawler Service.
Fig. 11: Third threat model We analyzed the validation algorithms of the Crawler
Service and identified the control flow path from the method
that pulls updated Wiki pages from Git, parses the Markdown
format of Wiki pages, and stores the parsed data in indexes. To
depict this setting via the trust boundaries Admin Project A and exploit this path, we generated a payload with SerialDetector,
Admin Project B. This is a typical scenario in cloud-based web stored the payload to the Wiki page, and waited for the Crawler
applications where a user can register a separate project and to transfer the payload to indexes and for the Search Engine
become the administrator of that project. A single application to deserialize the data using BinaryFormatter. However, the
process often serves several isolated projects. In this case, an exploitation failed, hence we attached a debugger to the Agent
attacker can register an administrator account for their own Service to identify the instructions that changed the payload.
project and exploit an OIV directly (arrow 1a) to gain access
to all resources of OS Account including the database and the The Crawler first validates that the Wiki page is a text
data of any other projects (arrow 2a). document. It uploads the file as a byte array and verifies that
the content uses Unicode encoding by checking the first bytes.
If the attacker has access only to a subset of features, e.g.,
The payload for BinaryFormatter always starts with the byte
a user with minimal privileges, they can exploit a chain of a
0x00 and the next 4 bytes contain an integer value of the
client-side and object injection vulnerabilities to carry out the
ID of the root serialized object. The Crawler accepts the one
attack. For example, the attacker can exploit an XSS vulnera-
sequence of the first bytes of the header that starts with 0x00 as
bility to run malicious JavaScript code into the administrator’s
Unicode format, and it is 0x0000FEFF. Thereby, we changed
browser and use it to relay the malicious payload to OIV code
the root ID of the payload to get the header to correspond
that is available only to the administrator (path 1b, 2b, 3b, 4b).
to Unicode format, tested a new payload for BinaryFormatter
using SerialDetector, and managed to bypass this validation.
C. SerialDetector in action
We run the exploit using the new payload and failed again.
We used SerialDetector to analyze the Azure DevOps
Following our human-in-the-loop approach, we started a new
Server against OIVs. We described templates of OIV in inse-
manual iteration of the “investigating, fixing and testing” loop.
cure serializers and run the exploitation phase of SerialDetector
The debugger revealed that the Crawler Service parses the Wiki
to determine which insecure serializers ADS uses. The tool
page as Markdown document and stores the parsed data to
analyzed the codebase of the application and built the Call
the index. Because we use the binary data instead of a valid
Graph from entry points to the given insecure methods. The
Markdown document, the parser rejected storing the document
analyzer handled 422 assemblies that contain 630,251 methods
to the indexes. However, when the parser throws an exception,
and 11,258,350 instructions. This analysis was completed
the Crawler Service stores the content of Wiki page to the
in 174 sec. Thereby, we detected an usage of 7 serializers
index as is. This allows us to transfer the payload to the
in the codebase of ADS: BinaryFormatter, DataContractSe-
BinaryFormatter via the indexes. We found a bug in Markdown
rializer, JavaScriptSerializer, Newtonsoft.Json, XamlReader,
parser which throws an exception for certain incorrect strings.
XmlSerializer, YamlDotNet. We have checked method calls of
We then added the string to the original payload, created and
DataContractSerializer, JavaScriptSerializer, Newtonsoft.Json,
tested the second version of the payload with SerialDetector,
XamlReader and XmlSerializer, and concluded that it is being
and run the exploit on ADS successfully. The attack propagates
used in the safe mode for untrusted data.
the payload from the attacker-controlled Git repository to
RCE via BinaryFormatter. The BinaryFormatter matched the input of the BinaryFormatter using Crawler and Storage
the patterns generated by SerialDetector, hence we could services, as depicted by the path 1b, 2b, 3b in Figure 10.
15
We have reported the vulnerability to Microsoft following requests with administrator privileges to ADS triggering the
the coordinated disclosure principles. Microsoft assigned CVE- deserialization of the malicious YAML file. Thus, the attacker
2019-1306 and released a patch to address the vulnerability. executes an RCE attack on the target ADS with minimal
The fix uses a look-ahead approach [16] to control class privileges (i.e., only access to the source code repository).
loading, depending on the type name. The .NET Framework
provides the class SerializationBinder that allows to use The second XSS targets a victim that opens a page with
the look-ahead approach by configuring BinaryFormatter with the test description. ADS uses the Test hub feature for tracking
a custom implementation of the binder. Thereby, a developer the manual testing of applications. It provides three main types
can create only safe types during deserialization and avoid of test management artifacts: test plans, test suites, and test
instantiating unsafe types. The fixed version filters out the cases. The test case description field had insufficient validation
types via a whitelist which prevents the OIV exploitation. and sanitization of the input text. The attacker may inject
JavaScript in the description field and get a stored XSS on
RCE via YamlDotNet. ADS uses the YAML format the Test Case page. When the administrator opens this page,
for describing pipelines to automatically build and test the the JavaScript code is executed in the administrator’s browser
code of projects. The YAML pipeline configuration file may allowing for requests to Web API with administrator privileges.
be stored in the source code repository of a project. ADS We exploited the vulnerability similarly to the RCE on the
uploads the configuration file from the repository, deserializes server. The path 1b, 2b, 3b, 4b illustrates the attack.
it, and queues a build task to the Build Agent. The agent
performs building and testing of the code from the repository We reported these vulnerabilities to Microsoft following
following the YAML configuration file. For security reasons, the coordinated disclosure principles. Microsoft assigned CVE-
the documentation recommends to run the agent in an isolated 2019-0866 and CVE-2019-0872 for each vulnerable attack
environment. Thus, code execution vulnerabilities during the chain and fixed it. The XSS vulnerabilities were fixed by
build and test process are not directly exploitable in a typical adding additional validation to the web page and by requiring
configuration. However, the Web Application of ADS performs users to download the PDF document instead of opening it
deserialization of the YAML file before running the agent. This in the browser. To prevent the OIV exploitation, Microsoft
boosts the impact of code execution in the Web Application to implemented their own lightweight YAML serializer using a
affect the entire system. For instance, an attacker can escalate parser from the YamlDotNet. This serializer does not allow
to privileges of the OS Account running the Web Application. to instantiate an object based on the type of information from
the file. It deserializes only a small predefined subset of types
We used SerialDetector to build the call graph of method which prevents the composition of a malicious gadget.
calls that reach the YamlDotNet deserialization methods. By
examining the entry points of the call graph, we found that the VIII. R ELATED WORKS
public Web API allows to run a build process using the YAML
configuration file. We generated a payload using SerialDetector This section discusses related works targeting object injec-
and ran the build process with our payload as the build tion vulnerabilities and injection vulnerabilities.
configuration. Upon failure of our first attempt, we started Object Injection Vulnerabilities. The closest related re-
the application debugging to identify a conditional statement search is the work of Dahse et al. [11], [13], which implements
causing the failure. The build configuration handler required static analysis to systematically detect gadgets in common PHP
small changes in the payload to pass it to the serializer. We applications. Like us, they implement static taint analysis to
just added the string --- as the first and the last payload lines. detect exploitable vulnerabilities. The key difference is that
However, YAML-based pipelines were a new experimental SerialDetector’s analysis operates at the assembly level to
feature at the moment and they were disabled by default. The discover new OIV patterns, while Dahse et al. target PHP
feature can be enabled by the administrator locally on the source code via well-known attack triggers (called magic
machine. We also found an undocumented Web API to enable methods in their setting). On the other hand, SerialDetector
the feature remotely, but such request requires administrator relies on known gadgets. An interesting avenue for future work
privileges in ADS. This scenario corresponds to the threat is to explore the complementary techniques by Dahse et al. to
model in Figure 11. One ADS instance supports few project implement gadget generation in SerialDetector.
collections with different user roles. However, the administra-
Shahriar and Haddad [40] propose a lightweight approach
tor of one collection may not have access to another collection.
based on latent semantic indexing to identify keywords that are
If the user with administrator privileges exploits the OIV and
likely responsible for OIVs and apply it systematically to PHP
triggers an RCE, this user can get access to the resources and
applications to find new vulnerabilities. Rasheed et al. [35]
data of all collections. The path 1a, 2a shows this attack.
study DoS vulnerabilities in YAML libraries across different
We demonstrated higher impact of the YamlDotNet OIV programming languages and discover several new vulnerabili-
by looking for XSS vulnerabilities. We found two XSSs using ties. Recently, Lekies et al. [28] showed that code-reuse attacks
static and manual analysis. The first one can be exploited when are feasible in the client-side web applications by proposing a
the victim opens a PDF file from the source code repository new attack vector that breaks all existing XSS mitigations via
using the ADS web interface. We use a weakness of Internet script gadgets. Cristalli et al. [10] propose a dynamic approach
Explorer to execute scripts embedded into PDF files (now this to identify trusted execution paths during a training phase
is also fixed). Thereby, an attacker needs to prepare a malicious with benign inputs, and leverages this information to detect
PDF file, upload it to the repository, and craft the link to the insecure deserialization via a lightweight sandbox. Hawkins
PDF file using the viewer of ADS. When the administrator and Demsky [23] present ZenIDS, a system to dynamically
opens this link in Internet Explorer, the embedded script sends learn the trusted execution paths of an application during an
16
online training period and report execution anomalies as po- ACKNOWLEDGMENT
tential intrusions. Dietrich et al. [14] investigate deserialization
vulnerabilities to exploit the topology of object graphs con- We thank the anonymous reviewers for useful feedback.
structed from Java classes in a way that leads dererialization The work was partly funded by the Swedish Research Council
to DOS attacks exhausting stack memory, heap memory, and (VR) under the project JointForce and by the Swedish Founda-
CPU time. SerialDetector focuses on generating OIV patterns tion for Strategic Research (SSF) under the project TrustFull.
targeting low level features of the framework and libraries. Our
results are complementary and can help improve the precision R EFERENCES
of these techniques. Moreover, to our best knowledge, none [1] “dnlib,” https://github.com/0xd4d/dnlib.
of the existing static analysis has been applied to complex [2] “Standard ECMA-335 Common Language Infrastructure (CLI),” https:
production software such as Azure DevOps Server. //www.ecma-international.org/publications/standards/Ecma-335.htm.
[3] “YSoSerial.Net,” https://github.com/pwntester/ysoserial.net.
Our work draws inspiration on exploitation techniques de-
veloped by the practitioners’ community [17], [18], [22], [32]. [4] S. Arzt, S. Rasthofer, C. Fritz, E. Bodden, A. Bartel, J. Klein,
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18