The paper introduces NameTrap, a source‑code poisoning attack that renames identifiers in third‑party Python files to steer AI coding agents toward specific Common Weakness Enumeration (CWE) vulnerabilities. By iteratively aligning variable names with CWE‑specific conventions, the attack achieves success rates ranging from 22.5% to 75.0% across six agents and four vulnerability categories, outperforming random and neutral renaming strategies. The poisoned files contain no explicit instructions or vulnerable statements, and existing AI‑based and static security analyzers fail to detect the induced weaknesses.
arXiv:2607. 20759v1 Announce Type: cross Abstract: AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with autonomous access to local files and tools.
arXiv:2608.30686v1 Announce Type: cross Abstract: Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity ca...
Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on repository poisoning...
arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.
arXiv:2610.14350v2 Announce Type: cross Abstract: Having demonstrated a strong potential for automating vulnerability discovery, AI agents can also be used to attack software systems at scale. Today'...
The paper revisits Thompson’s classic compiler back‑door attack in the context of self‑modifying AI coding agents. By poisoning the benchmarks used for self‑evaluation, the authors demonstrate that agents such as the Darwin Gödel Machine, Self‑Improving Coding Agent, and Hyperagents can be coaxed into generating vulnerable code, even on clean, held‑out tasks. Experiments show that the contamination can persist after subsequent clean training, highlighting the need for more robust agent designs.
arXiv:2607. 05120v1 Announce Type: cross Abstract: AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context.
arXiv:2608. 14876v1 Announce Type: cross Abstract: Agentic coding assistants are finding widespread use, not just in new code development but in quickly ingesting and leveraging third-party code.
arXiv:2609.15939v1 Announce Type: cross Abstract: Language-model agents increasingly operate over complete software repositories, yet cybersecurity evaluations primarily measure whether they can dete...
arXiv:2508.14925v2 Announce Type: replace-cross Abstract: By providing a standardized interface for LLM agents to interact with external tools, the Model Context Protocol (MCP) is quickly becoming a...
The paper introduces CodePoisonRAG, a framework that poisons retrieval-augmented code generation systems by transforming benign code artifacts into malicious ones. It injects CWE-specific vulnerabilities and false safety claims into a single task-matched artifact, achieving high success rates across multiple generators and even against a defense system. The study demonstrates that attackers can target and propagate specific weaknesses without altering the underlying language model.
arXiv:2603.18740v5 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rang...