arXiv subjects

Martin Monperrus

Publications and source records attributed to Martin Monperrus.

At least 37 records · Page 2Linked to original sources

arXiv2026

PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts

Smart contracts operate in a highly adversarial environment, where vulnerabilities can lead to substantial financial losses. Thus, smart contracts are subject to security audits. In auditing, proof-of-concept (PoC) exploits play a critical role by demonstrating to the stakeholders that the reported vulnerabilities are genuine, reproducible, and actionable. However, manually creating PoCs is time-consuming, error-prone, and often constrained by tight audit schedules. We introduce PoCo, an agentic framework that automatically generates executable PoC exploits from natural-language vulnerability descriptions written by auditors. PoCo autonomously generates PoC exploits in an agentic manner by interacting with a set of codeexecution tools in a Reason-Act-Observe loop. It produces fully executable exploits compatible with the Foundry testing framework, ready for integration into audit reports and other security tools. We evaluate PoCo on a dataset of 23 real-world vulnerability reports. PoCo consistently outperforms the Zero-shot and Workflow baselines, generating well-formed and logically correct PoCs. Our results demonstrate that agentic frameworks can significantly reduce the effort required for high-quality PoCs in smart contract audits. Our contribution provides actionable knowledge for the smart contract security community.

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arXiv2026

Byam: Fixing Breaking Dependency Updates with Large Language Models

Application Programming Interfaces (APIs) facilitate the integration of third-party dependencies within the code of client applications. However, changes to an API, such as deprecation, modification of parameter names or types, or complete replacement with a new API, can break existing client code. These changes are called breaking dependency updates; It is often tedious for API users to identify the cause of these breaks and update their code accordingly. In this paper, we explore the use of Large Language Models (LLMs) to automate client code updates in response to breaking dependency updates. We evaluate our approach on the BUMP dataset, a benchmark for breaking dependency updates in Java projects. Our approach leverages LLMs with advanced prompts, including information from the build process and from the breaking dependency analysis. We assess effectiveness at three granularity levels: at the build level, the file level, and the individual compilation error level. We experiment with five LLMs: Google Gemini-2.0 Flash, OpenAI GPT4o-mini, OpenAI o3-mini, Alibaba Qwen2.5-32b-instruct, and DeepSeek V3. Our results show that LLMs can automatically repair breaking updates. Among the considered models, OpenAI's o3-mini is the best, able to completely fix 27% of the builds when using prompts that include contextual information such as the buggy line, API differences, error messages, and step-by-step reasoning instructions. Also, it fixes 78% of the individual compilation errors. Overall, our findings demonstrate the potential for LLMs to fix compilation errors due to breaking dependency updates, supporting developers in their efforts to stay up-to-date with changes in their dependencies.

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arXiv2026

Verifiable Provenance of Software Artifacts with Zero-Knowledge Compilation

Verifying that a compiled binary originates from its claimed source code is a fundamental security requirement, called source code provenance. Achieving verifiable source code provenance in practice remains challenging. The most popular technique, called reproducible builds, requires difficult matching and reexecution of build toolchains and environments. We propose a novel approach to verifiable provenance based on compiling software with zero-knowledge virtual machines (zkVMs). By executing a compiler within a zkVM, our system produces both the compiled output and a cryptographic proof attesting that the compilation was performed on the claimed source code with the claimed compiler. We implement a proof-of-concept implementation using the RISC Zero zkVM and the ChibiCC C compiler, and evaluate it on 200 synthetic programs as well as 31 OpenSSL and 21 libsodium source files. Our results show that zk-compilation is applicable to real-world software and provides strong security guarantees: all adversarial tests targeting compiler substitution, source tampering, output manipulation, and replay attacks are successfully blocked.

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arXiv2026

Prometheus: Towards Long-Horizon Codebase Navigation for Repository-Level Problem Solving

Large Language Models (LLMs) have shown remarkable capabilities in automating software engineering tasks, spurring the emergence of coding agents that scaffold LLMs with external tools to resolve repository-level problems. However, existing agents still struggle to navigate large-scale codebases, as the Needle-in-a-Haystack problem persists even with million-token context windows, where relevant evidence is often overwhelmed by large volumes of irrelevant code and documentation. Prior codebase navigation approaches, including embedding-based retrieval, file-system exploration, and graph-based retrieval, address parts of this challenge but fail to capture the temporal continuity of agent reasoning, rendering agents stateless and causing repeated repository traversals that hinder scalable planning and reasoning. To address these limitations, we present Prometheus, a memory-centric coding agent framework for long-horizon codebase navigation. Prometheus represents the repository as a unified knowledge graph to encode semantic dependencies and employs a context engine augmented with working memory that retains and reuses previously explored contexts to ensure continuity across reasoning steps. Built upon this engine, Prometheus integrates memory-enhanced navigation into a multi-agent system for automated issue resolution, encompassing issue classification, bug reproduction, patch generation, and verification. Comprehensive experiments are conducted on two widely used issue resolution benchmarks, i.e., SWE-bench Verified and SWE-PolyBench Verified. Powered by GPT-5, Prometheus achieves state-of-the-art performance with 74.4% and 33.8% resolution rates on the two benchmarks, ranking Top-6 and Top-1 among open-source agent systems, respectively. Our data and code are available at https://github.com/EuniAI/Prometheus.

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arXiv2025

Serializing Java Objects in Plain Code

In managed languages, serialization of objects is typically done in bespoke binary formats such as Protobuf, or markup languages such as XML or JSON. The major limitation of these formats is readability. Human developers cannot read binary code, and in most cases, suffer from the syntax of XML or JSON. This is a major issue when objects are meant to be embedded and read in source code, such as in test cases. To address this problem, we propose plain-code serialization. Our core idea is to serialize objects observed at runtime in the native syntax of a programming language. We realize this vision in the context of Java, and demonstrate a prototype which serializes Java objects to Java source code. The resulting source faithfully reconstructs the objects seen at runtime. Our prototype is called ProDJ and is publicly available. We experiment with ProDJ to successfully plain-code serialize 174,699 objects observed during the execution of 4 open-source Java applications. Our performance measurement shows that the performance impact is not noticeable. Through a user study, we demonstrate that developers prefer plain-code serialized objects within automatically generated tests over their representations as XML or JSON.

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arXiv2025

Software Supply Chain Security of Web3

Web3 applications, built on blockchain technology, manage billions of dollars in digital assets through decentralized applications (dApps) and smart contracts. These systems rely on complex, software supply chains that introduce significant security vulnerabilities. This paper examines the software supply chain security challenges unique to the Web3 ecosystem, where traditional Web2 software supply chain problems intersect with the immutable and high-stakes nature of blockchain technology. We analyze the threat landscape and propose mitigation strategies to strengthen the security posture of Web3 systems.

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arXiv2025

Causes and Canonicalization of Unreproducible Builds in Java

The increasing complexity of software supply chains and the rise of supply chain attacks have elevated concerns around software integrity. Users and stakeholders face significant challenges in validating that a given software artifact corresponds to its declared source. Reproducible Builds address this challenge by ensuring that independently performed builds from identical source code produce identical binaries. However, achieving reproducibility at scale remains difficult, especially in Java, due to a range of non-deterministic factors and caveats in the build process. In this work, we focus on reproducibility in Java-based software, archetypal of enterprise applications. We introduce a conceptual framework for reproducible builds, we analyze a large dataset from Reproducible Central, and we develop a novel taxonomy of six root causes of unreproducibility. We study actionable mitigations: artifact and bytecode canonicalization using OSS-Rebuild and jNorm respectively. Finally, we present Chains-Rebuild, a tool that achieve successfulcanonicalization for 26.60% on 12,803 unreproducible artifacts To sum up, our contributions are the first large-scale taxonomy of build unreproducibility causes in Java, a publicly available dataset of unreproducible builds, and Chains-Rebuild, a canonicalization tool for mitigating unreproducible builds in Java.

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arXiv2025

Maven-Hijack: Software Supply Chain Attack Exploiting Packaging Order

Java projects frequently rely on package managers such as Maven to manage complex webs of external dependencies. While these tools streamline development, they also introduce subtle risks to the software supply chain. In this paper, we present Maven-Hijack, a novel attack that exploits the order in which Maven packages dependencies and the way the Java Virtual Machine resolves classes at runtime. By injecting a malicious class with the same fully qualified name as a legitimate one into a dependency that is packaged earlier, an attacker can silently override core application behavior without modifying the main codebase or library names. We demonstrate the real-world feasibility of this attack by compromising the Corona-Warn-App, a widely used open-source COVID-19 contact tracing system, and gaining control over its database connection logic. We evaluate three mitigation strategies, such as sealed JARs, Java Modules, and the Maven Enforcer plugin. Our results show that, while Java Modules offer strong protection, the Maven Enforcer plugin with duplicate class detection provides the most practical and effective defense for current Java projects. These findings highlight the urgent need for improved safeguards in Java's build and dependency management processes to prevent stealthy supply chain attacks.

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arXiv2025

Galapagos: Automated N-Version Programming with LLMs

N-Version Programming is a well-known methodology for developing fault-tolerant systems. It achieves fault detection and correction at runtime by adding diverse redundancy into programs, minimizing fault mode overlap between redundant program variants. In this work, we propose the automated generation of program variants using large language models. We design, develop and evaluate Galápagos: a tool for generating program variants using LLMs, validating their correctness and equivalence, and using them to assemble N-Version binaries. We evaluate Galápagos by creating N-Version components of real-world C code. Our original results show that Galápagos can produce program variants that are proven to be functionally equivalent, even when the variants are written in a different programming language. Our systematic diversity measurement indicates that functionally equivalent variants produced by Galápagos, are statically different after compilation, and present diverging internal behavior at runtime. We demonstrate that the variants produced by Galápagos can protect C code against real miscompilation bugs which affect the Clang compiler. Overall, our paper shows that producing N-Version software can be drastically automated by advanced usage of practical formal verification and generative language models.

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arXiv2025

The Design Space of Lockfiles Across Package Managers

Software developers reuse third-party packages that are hosted in package registries. At build time, a package manager resolves and fetches the direct and indirect dependencies of a project. Most package managers also generate a lockfile, which records the exact set of resolved dependency versions. Lockfiles are used to reduce build times; to verify the integrity of resolved packages; and to support build reproducibility across environments and time. Despite these beneficial features, developers often struggle with their maintenance, usage, and interpretation. In this study, we unveil the major challenges related to lockfiles, such that future researchers and engineers can address them. We perform the first comprehensive study of lockfiles across 7 popular package managers, npm, pnpm, Cargo, Poetry, Pipenv, Gradle, and Go. First, we highlight the wide variety of design decisions that package managers make, regarding the generation process as well as the content of lockfiles. Next, we conduct a qualitative analysis based on semi-structured interviews with 15 developers. We capture first-hand insights about the benefits that developers perceive in lockfiles, as well as the challenges they face to manage these files. Following these observations, we make 5 recommendations to further improve lockfiles, for a better developer experience.

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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

Maven-Lockfile: High Integrity Rebuild of Past Java Releases

Modern software projects depend on many third-party libraries, complicating reproducible and secure builds. Several package managers address this with the generation of a lockfile that freezes dependency versions and can be used to verify the integrity of dependencies. Yet, Maven, one of the most important package managers in the Java ecosystem, lacks native support for a lockfile. We present Maven-Lockfile to generate and update lockfiles, with support for rebuilding projects from past versions. Our lockfiles capture all direct and transitive dependencies with their checksums, enabling high integrity builds. Our evaluation shows that Maven-Lockfile can reproduce builds from historical commits and is able to detect tampered artifacts. With minimal configuration, Maven-Lockfile equips Java projects with modern build integrity and build reproducibility, and fosters future research on software supply chain security in Java.

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arXiv2025

Proving and Rewarding Client Diversity to Strengthen Resilience of Blockchain Networks

Client diversity is a cornerstone of blockchain resilience, yet most networks suffer from a dangerously skewed distribution of client implementations. This monoculture exposes the network to very risky scenarios, such as massive financial losses in the event of a majority client failure. In this paper, we present a novel framework that combines verifiable execution and economic incentives to provably identify and reward the use of minority clients, thereby promoting a healthier, more robust ecosystem. Our approach leverages state-of-the-art verifiable computation (zkVMs and TEEs) to generate cryptographic proofs of client execution, which are then verified on-chain. We design and implement an end-to-end prototype of verifiable client diversity in the context of Ethereum, by modifying the popular Lighthouse client and by deploying our novel diversity-aware reward protocol. Through comprehensive experiments, we quantify the practicality of our approach, from overheads of proof production and verification to the effectiveness of the incentive mechanism. This work demonstrates, for the first time, a practical and economically viable path to encourage and ensure provable client diversity in blockchain networks. Our findings inform the design of future protocols that seek to maximize the resilience of decentralized systems

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arXiv2025

RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair

Automated Program Repair (APR) has evolved significantly with the advent of Large Language Models (LLMs). Fine-tuning LLMs for program repair is a recent avenue of research, with many dimensions which have not been explored. Existing work mostly fine-tune LLMs with naive code representations and does not scale to frontier models. To address this problem, we propose RepairLLaMA, a novel program repair approach that 1) identifies optimal code representations for APR with fine-tuned models, and 2) pioneers state-of-the-art parameter-efficient fine-tuning technique (PEFT) for program repair. This results in RepairLLaMA producing a highly effective `program repair adapter' for fixing bugs with AI. Our experiments demonstrate the validity of both concepts. First, fine-tuning adapters with program repair specific code representations enables the model to use meaningful repair signals and produce better patches. Second, parameter-efficient fine-tuning helps fine-tuning to converge and clearly contributes to the effectiveness of RepairLLaMA in fixing bugs outside the fine-tuning data distribution. Overall, RepairLLaMA correctly fixes 144 Defects4J v2, 109 HumanEval-Java, and 20 GitBug-Java bugs, outperforming all baselines.

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arXiv2025

Mokav: Execution-driven Differential Testing with LLMs

It is essential to detect functional differences between programs in various software engineering tasks, such as automated program repair, mutation testing, and code refactoring. The problem of detecting functional differences between two programs can be reduced to searching for a difference exposing test (DET): a test input that results in different outputs on the subject programs. In this paper, we propose Mokav, a novel execution-driven tool that leverages LLMs to generate DETs. Mokav takes two versions of a program (P and Q) and an example test input. When successful, Mokav generates a valid DET, a test input that leads to provably different outputs on P and Q. Mokav iteratively prompts an LLM with a specialized prompt to generate new test inputs. At each iteration, Mokav provides execution-based feedback from previously generated tests until the LLM produces a DET. We evaluate Mokav on 1535 pairs of Python programs collected from the Codeforces competition platform and 32 pairs of programs from the QuixBugs dataset. Our experiments show that Mokav outperforms the state-of-the-art, Pynguin and Differential Prompting, by a large margin. Mokav can generate DETs for 81.7% (1,255/1535) of the program pairs in our benchmark (versus 4.9% for Pynguin and 37.3% for Differential Prompting). We demonstrate that the iterative and execution-driven feedback components of the system contribute to its high effectiveness.

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arXiv2025

Do Automated Fixes Truly Mitigate Smart Contract Exploits?

Automated Program Repair (APR) for smart contract security promises to automatically mitigate smart contract vulnerabilities responsible for billions in financial losses. However, the true effectiveness of this research in addressing smart contract exploits remains uncharted territory. This paper bridges this critical gap by introducing a novel and systematic experimental framework for evaluating exploit mitigation of program repair tools for smart contracts. We qualitatively and quantitatively analyze 20 state-of-the-art APR tools using a dataset of 143 vulnerable smart contracts, for which we manually craft 91 executable exploits. We are the very first to define and measure the essential "exploit mitigation rate" , giving researchers and practitioners a real sense of effectiveness of cutting edge techniques. Our findings reveal substantial disparities in the state of the art, with an exploit mitigation rate ranging from a low of 29% to a high of 74%. Our study identifies systemic limitations, such as inconsistent functionality preservation, that must be addressed in future research on program repair for smart contracts.

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arXiv2025

GoLeash: Mitigating Golang Software Supply Chain Attacks with Runtime Policy Enforcement

Modern software supply chain attacks consist of introducing new, malicious capabilities into trusted third-party software components, in order to propagate to a victim through a package dependency chain. These attacks are especially concerning for the Go language ecosystem, which is extensively used in critical cloud infrastructures. We present GoLeash, a novel system that applies the principle of least privilege at the package-level granularity, by enforcing distinct security policies for each package in the supply chain. This finer granularity enables GoLeash to detect malicious packages more precisely than traditional sandboxing that handles security policies at process- or container-level. Moreover, GoLeash remains effective under obfuscation, can overcome the limitations of static analysis, and incurs acceptable runtime overhead.

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arXiv2025

On-Chain Analysis of Smart Contract Dependency Risks on Ethereum

In this paper, we present the first large-scale empirical study of smart contract dependencies, analyzing over 41 million contracts and 11 billion interactions on Ethereum up to December 2024. Our results yield four key insights: (1) 59% of contract transactions involve multiple contracts (median of 4 per transaction in 2024) indicating potential smart contract dependency risks; (2) the ecosystem exhibits extreme centralization, with just 11 (0.001%) deployers controlling 20.5 million (50%) of alive contracts, with major risks related to factory contracts and deployer privileges; (3) three most depended-upon contracts are mutable, meaning large parts of the ecosystem rely on contracts that can be altered at any time, which is a significant risk, (4) actual smart contract protocol dependencies are significantly more complex than officially documented, undermining Ethereum's transparency ethos, and creating unnecessary attack surface. Our work provides the first large-scale empirical foundation for understanding smart contract dependency risks, offering crucial insights for developers, users, and security researchers in the blockchain space.

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