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Alexandre Bartel

Publications and source records attributed to Alexandre Bartel.

At least 19 recordsLinked to original sources
arXiv2025

Raven: Mining Defensive Patterns in Ethereum via Semantic Transaction Revert Invariants Categories

We frame Ethereum transactions reverted by invariants-require( )/ assert( )/if ( ) revert statements in the contract implementation-as a positive signal of active on-chain defenses. Despite their value, the defensive patterns in these transactions remain undiscovered and underutilized in security research. We present Raven, a framework that aligns reverted transactions to the invariant causing the reversion in the smart contract source code, embeds these invariants using our BERT-based fine-tuned model, and clusters them by semantic intent to mine defensive invariant categories on Ethereum. Evaluated on a sample of 20,000 reverted transactions, Raven achieves cohesive and meaningful clusters of transaction-reverting invariants. Manual expert review of the mined 19 semantic clusters uncovers six new invariant categories absent from existing invariant catalogs, including feature toggles, replay prevention, proof/signature verification, counters, caller-provided slippage thresholds, and allow/ban/bot lists. To demonstrate the practical utility of this invariant catalog mining pipeline, we conduct a case study using one of the newly discovered invariant categories as a fuzzing oracle to detect vulnerabilities in a real-world attack. Raven thus can map Ethereum's successful defenses. These invariant categories enable security researchers to develop analysis tools based on data-driven security oracles extracted from the smart contracts' working defenses.

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arXiv2025

CFIghter: Automated Control-Flow Integrity Enablement and Evaluation for Legacy C/C++ Systems

Compiler-based Control-Flow Integrity (CFI) offers strong forward-edge protection but remains challenging to deploy in large C/C++ software due to visibility mismatches, type inconsistencies, and unintended behavioral failures. We present CFIghter, the first fully automated system that enables strict, type-based CFI in real-world projects by detecting, classifying, and repairing unintended policy violations exposed by the test suite. CFIghter integrates whole-program analysis with guided runtime monitoring and iteratively applies the minimal necessary adjustments to CFI enforcement only where required, stopping once all tests pass or remaining failures are deemed unresolvable. We evaluate CFIghter on four GNU projects. It resolves all visibility-related build errors and automatically repairs 95.8% of unintended CFI violations in the large, multi-library util-linux codebase, while retaining strict enforcement at over 89% of indirect control-flow sites. Across all subjects, CFIghter preserves strict type-based CFI for the majority of the codebase without requiring manual source-code changes, relying only on automatically generated visibility adjustments and localized enforcement scopes where necessary. These results show that automated compatibility repair makes strict compiler CFI practically deployable in mature, modular C software.

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arXiv2025

FLAMES: Fine-tuning LLMs to Synthesize Invariants for Smart Contract Security

Smart contract vulnerabilities cost billions of dollars annually, yet existing automated analysis tools fail to generate deployable defenses. We present FLAMES, a novel automated approach that synthesizes executable runtime guards as Solidity "require" statements to harden smart contracts against exploits. Unlike prior work that relies on vulnerability labels, symbolic analysis, or natural language specifications, FLAMES employs domain-adapted large language models trained through fill-in-the-middle supervised fine-tuning on real-world invariants extracted from 514,506 verified contracts. Our extensive evaluation across three dimensions demonstrates FLAMES's effectiveness: (1) Compilation: FLAMES achieves 96.7% compilability for synthesized invariant (2) Semantic Quality: on a curated test set of 5,000 challenging invariants, FLAMES produces exact or semantically equivalent matches to ground truth in 44.5% of cases; (3) Exploit Mitigation: FLAMES prevents 22 out of 108 real exploits (20.4%) while preserving contract functionality, and (4) FLAMES successfully blocks the real-world APEMAGA incident by synthesizing a pre-condition that mitigates the attack. FLAMES establishes that domain-adapted LLMs can automatically generate production-ready security defenses for smart contracts without requiring vulnerability detection, formal specifications, or human intervention. We release our code, model weights, datasets, and evaluation infrastructure to enable reproducible research in this critical domain.

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arXiv2025

A Practical Guideline and Taxonomy to LLVM's Control Flow Integrity

Memory corruption vulnerabilities remain one of the most severe threats to software security. They often allow attackers to achieve arbitrary code execution by redirecting a vulnerable program's control flow. While Control Flow Integrity (CFI) has gained traction to mitigate this exploitation path, developers are not provided with any direction on how to apply CFI to real-world software. In this work, we establish a taxonomy mapping LLVM's forward-edge CFI variants to memory corruption vulnerability classes, offering actionable guidance for developers seeking to deploy CFI incrementally in existing codebases. Based on the Top 10 Known Exploited Vulnerabilities (KEV) list, we identify four high-impact vulnerability categories and select one representative CVE for each. We evaluate LLVM's CFI against each CVE and explain why CFI blocks exploitation in two cases while failing in the other two, illustrating its potential and current limitations. Our findings support informed deployment decisions and provide a foundation for improving the practical use of CFI in production systems.

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arXiv2025

Sleeping Giants -- Activating Dormant Java Deserialization Gadget Chains through Stealthy Code Changes

Java deserialization gadget chains are a well-researched critical software weakness. The vast majority of known gadget chains rely on gadgets from software dependencies. Furthermore, it has been shown that small code changes in dependencies have enabled these gadget chains. This makes gadget chain detection a purely reactive endeavor. Even if one dependency's deployment pipeline employs gadget chain detection, a gadget chain can still result from gadgets in other dependencies. In this work, we assess how likely small code changes are to enable a gadget chain. These changes could either be accidental or intentional as part of a supply chain attack. Specifically, we show that class serializability is a strongly fluctuating property over a dependency's evolution. Then, we investigate three change patterns by which an attacker could stealthily introduce gadgets into a dependency. We apply these patterns to 533 dependencies and run three state-of-the-art gadget chain detectors both on the original and the modified dependencies. The tools detect that applying the modification patterns can activate/inject gadget chains in 26.08% of the dependencies we selected. Finally, we verify the newly detected chains. As such, we identify dormant gadget chains in 53 dependencies that could be added through minor code modifications. This both shows that Java deserialization gadget chains are a broad liability to software and proves dormant gadget chains as a lucrative supply chain attack vector.

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arXiv2025

In the Magma chamber: Update and challenges in ground-truth vulnerabilities revival for automatic input generator comparison

Fuzzing is a well-established technique for detecting bugs and vulnerabilities. With the surge of fuzzers and fuzzer platforms being developed such as AFL and OSSFuzz rises the necessity to benchmark these tools' performance. A common problem is that vulnerability benchmarks are based on bugs in old software releases. For this very reason, Magma introduced the notion of forward-porting to reintroduce vulnerable code in current software releases. While their results are promising, the state-of-the-art lacks an update on the maintainability of this approach over time. Indeed, adding the vulnerable code to a recent software version might either break its functionality or make the vulnerable code no longer reachable. We characterise the challenges with forward-porting by reassessing the portability of Magma's CVEs four years after its release and manually reintroducing the vulnerabilities in the current software versions. We find the straightforward process efficient for 17 of the 32 CVEs in our study. We further investigate why a trivial forward-porting process fails in the 15 other CVEs. This involves identifying the commits breaking the forward-porting process and reverting them in addition to the bug fix. While we manage to complete the process for nine of these CVEs, we provide an update on all 15 and explain the challenges we have been confronted with in this process. Thereby, we give the basis for future work towards a sustainable forward-ported fuzzing benchmark.

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arXiv2025

Deserialization Gadget Chains are not a Pathological Problem in Android:an In-Depth Study of Java Gadget Chains in AOSP

Inter-app communication is a mandatory and security-critical functionality of operating systems, such as Android. On the application level, Android implements this facility through Intents, which can also transfer non-primitive objects using Java's Serializable API. However, the Serializable API has a long history of deserialization vulnerabilities, specifically deserialization gadget chains. Research endeavors have been heavily directed towards the detection of deserialization gadget chains on the Java platform. Yet, there is little knowledge about the existence of gadget chains within the Android platform. We aim to close this gap by searching gadget chains in the Android SDK, Android's official development libraries, as well as frequently used third-party libraries. To handle this large dataset, we design a gadget chain detection tool optimized for soundness and efficiency. In a benchmark on the full Ysoserial dataset, it achieves similarly sound results to the state-of-the-art in significantly less time. Using our tool, we first show that the Android SDK contains almost the same trampoline gadgets as the Java Class Library. We also find that one can trigger Java native serialization through Android's Parcel API. Yet, running our tool on the Android SDK and 1,200 Android dependencies, in combination with a comprehensive sink dataset, yields no security-critical gadget chains. This result opposes the general notion of Java deserialization gadget chains being a widespread problem. Instead, the issue appears to be more nuanced, and we provide a perspective on where to direct further research.

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arXiv2022

An In-depth Study of Java Deserialization Remote-Code Execution Exploits and Vulnerabilities

Nowadays, an increasing number of applications uses deserialization. This technique, based on rebuilding the instance of objects from serialized byte streams, can be dangerous since it can open the application to attacks such as remote code execution (RCE) if the data to deserialize is originating from an untrusted source. Deserialization vulnerabilities are so critical that they are in OWASP's list of top 10 security risks for web applications. This is mainly caused by faults in the development process of applications and by flaws in their dependencies, i.e., flaws in the libraries used by these applications. No previous work has studied deserialization attacks in-depth: How are they performed? How are weaknesses introduced and patched? And for how long are vulnerabilities present in the codebase? To yield a deeper understanding of this important kind of vulnerability, we perform two main analyses: one on attack gadgets, i.e., exploitable pieces of code, present in Java libraries, and one on vulnerabilities present in Java applications. For the first analysis, we conduct an exploratory large-scale study by running 256515 experiments in which we vary the versions of libraries for each of the 19 publicly available exploits. Such attacks rely on a combination of gadgets present in one or multiple Java libraries. A gadget is a method which is using objects or fields that can be attacker-controlled. Our goal is to precisely identify library versions containing gadgets and to understand how gadgets have been introduced and how they have been patched. We observe that the modification of one innocent-looking detail in a class -- such as making it public -- can already introduce a gadget. Furthermore, we noticed that among the studied libraries, 37.5% are not patched, leaving gadgets available for future attacks. For the second analysis, we manually analyze 104 deserialization vulnerabilities CVEs to understand how vulnerabilities are introduced and patched in real-life Java applications. Results indicate that the vulnerabilities are not always completely patched or that a workaround solution is proposed. With a workaround solution, applications are still vulnerable since the code itself is unchanged.

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arXiv2021

On The (In)Effectiveness of Static Logic Bomb Detector for Android Apps

Android is present in more than 85% of mobile devices, making it a prime target for malware. Malicious code is becoming increasingly sophisticated and relies on logic bombs to hide itself from dynamic analysis. In this paper, we perform a large scale study of TSOPEN, our open-source implementation of the state-of-the-art static logic bomb scanner TRIGGERSCOPE, on more than 500k Android applications. Results indicate that the approach scales. Moreover, we investigate the discrepancies and show that the approach can reach a very low false-positive rate, 0.3%, but at a particular cost, e.g., removing 90% of sensitive methods. Therefore, it might not be realistic to rely on such an approach to automatically detect all logic bombs in large datasets. However, it could be used to speed up the location of malicious code, for instance, while reverse engineering applications. We also present TRIGDB a database of 68 Android applications containing trigger-based behavior as a ground-truth to the research community.

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arXiv2021

RAICC: Revealing Atypical Inter-Component Communication in Android Apps

Inter-Component Communication (ICC) is a key mechanism in Android. It enables developers to compose rich functionalities and explore reuse within and across apps. Unfortunately, as reported by a large body of literature, ICC is rather "complex and largely unconstrained", leaving room to a lack of precision in apps modeling. To address the challenge of tracking ICCs within apps, state of the art static approaches such as Epicc, IccTA and Amandroid have focused on the documented framework ICC methods (e.g., startActivity) to build their approaches. In this work we show that ICC models inferred in these state of the art tools may actually be incomplete: the framework provides other atypical ways of performing ICCs. To address this limitation in the state of the art, we propose RAICC a static approach for modeling new ICC links and thus boosting previous analysis tasks such as ICC vulnerability detection, privacy leaks detection, malware detection, etc. We have evaluated RAICC on 20 benchmark apps, demonstrating that it improves the precision and recall of uncovered leaks in state of the art tools. We have also performed a large empirical investigation showing that Atypical ICC methods are largely used in Android apps, although not necessarily for data transfer. We also show that RAICC increases the number of ICC links found by 61.6% on a dataset of real-world malicious apps, and that RAICC enables the detection of new ICC vulnerabilities.

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arXiv2019

ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware

Billions of users rely on the security of the Android platform to protect phones, tablets, and many different types of consumer electronics. While Android's permission model is well studied, the enforcement of the protection policy has received relatively little attention. Much of this enforcement is spread across system services, taking the form of hard-coded checks within their implementations. In this paper, we propose Authorization Check Miner (ACMiner), a framework for evaluating the correctness of Android's access control enforcement through consistency analysis of authorization checks. ACMiner combines program and text analysis techniques to generate a rich set of authorization checks, mines the corresponding protection policy for each service entry point, and uses association rule mining at a service granularity to identify inconsistencies that may correspond to vulnerabilities. We used ACMiner to study the AOSP version of Android 7.1.1 to identify 28 vulnerabilities relating to missing authorization checks. In doing so, we demonstrate ACMiner's ability to help domain experts process thousands of authorization checks scattered across millions of lines of code.

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arXiv2017

AndroZoo++: Collecting Millions of Android Apps and Their Metadata for the Research Community

We present a growing collection of Android apps collected from several sources, including the official Google Play app market and a growing collection of various metadata of those collected apps aiming at facilitating the Android-relevant research works. Our dataset by far has collected over five million apps and over 20 types of metadata such as VirusTotal reports. Our objective of collecting this dataset is to contribute to ongoing research efforts, as well as to enable new potential research topics on Android Apps. By releasing our app and metadata set to the research community, we also aim at encouraging our fellow researchers to engage in reproducible experiments. This article will be continuously updated based on the growing apps and metadata collected in the AndroZoo project. If you have specific metadata that you want to collect from AndroZoo and which are not yet provided by far, please let us know. We will thereby prioritise it in our collecting process so as to provide it to our fellow researchers in a short manner.

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arXiv2016

Analyzing the Gadgets Towards a Metric to Measure Gadget Quality

Current low-level exploits often rely on code-reuse, whereby short sections of code (gadgets) are chained together into a coherent exploit that can be executed without the need to inject any code. Several protection mechanisms attempt to eliminate this attack vector by applying code transformations to reduce the number of available gadgets. Nevertheless, it has emerged that the residual gadgets can still be sufficient to conduct a successful attack. Crucially, the lack of a common metric for "gadget quality" hinders the effective comparison of current mitigations. This work proposes four metrics that assign scores to a set of gadgets, measuring quality, usefulness, and practicality. We apply these metrics to binaries produced when compiling programs for architectures implementing Intel's recent MPX CPU extensions. Our results demonstrate a 17% increase in useful gadgets in MPX binaries, and a decrease in side-effects and preconditions, making them better suited for ROP attacks.

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arXiv2014

Static Analysis for Extracting Permission Checks of a Large Scale Framework: The Challenges And Solutions for Analyzing Android

A common security architecture is based on the protection of certain resources by permission checks (used e.g., in Android and Blackberry). It has some limitations, for instance, when applications are granted more permissions than they actually need, which facilitates all kinds of malicious usage (e.g., through code injection). The analysis of permission-based framework requires a precise mapping between API methods of the framework and the permissions they require. In this paper, we show that naive static analysis fails miserably when applied with off-the-shelf components on the Android framework. We then present an advanced class-hierarchy and field-sensitive set of analyses to extract this mapping. Those static analyses are capable of analyzing the Android framework. They use novel domain specific optimizations dedicated to Android.

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arXiv2014

I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis

Android applications may leak privacy data carelessly or maliciously. In this work we perform inter-component data-flow analysis to detect privacy leaks between components of Android applications. Unlike all current approaches, our tool, called IccTA, propagates the context between the components, which improves the precision of the analysis. IccTA outperforms all other available tools by reaching a precision of 95.0% and a recall of 82.6% on DroidBench. Our approach detects 147 inter-component based privacy leaks in 14 applications in a set of 3000 real-world applications with a precision of 88.4%. With the help of ApkCombiner, our approach is able to detect inter-app based privacy leaks.

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arXiv2013

In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments

In this paper we claim that an efficient and readily applicable means to improve privacy of Android applications is: 1) to perform runtime monitoring by instrumenting the application bytecode and 2) in-vivo, i.e. directly on the smartphone. We present a tool chain to do this and present experimental results showing that this tool chain can run on smartphones in a reasonable amount of time and with a realistic effort. Our findings also identify challenges to be addressed before running powerful runtime monitoring and instrumentations directly on smartphones. We implemented two use-cases leveraging the tool chain: BetterPermissions, a fine-grained user centric permission policy system and AdRemover an advertisement remover. Both prototypes improve the privacy of Android systems thanks to in-vivo bytecode instrumentation.

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arXiv2013

Automatically Securing Permission-Based Software by Reducing the Attack Surface: An Application to Android

A common security architecture, called the permission-based security model (used e.g. in Android and Blackberry), entails intrinsic risks. For instance, applications can be granted more permissions than they actually need, what we call a "permission gap". Malware can leverage the unused permissions for achieving their malicious goals, for instance using code injection. In this paper, we present an approach to detecting permission gaps using static analysis. Our prototype implementation in the context of Android shows that the static analysis must take into account a significant amount of platform-specific knowledge. Using our tool on two datasets of Android applications, we found out that a non negligible part of applications suffers from permission gaps, i.e. does not use all the permissions they declare.

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