arXiv:2606.13757v1 Announce Type: cross Abstract: Large language model (LLM) reviewers are increasingly used in pull-request (PR) workflows, where their approvals help decide which code is merged into a repository. This raises a question that benchmarks for static vulnerability detection or code generation do not address: can an automated reviewer reject a malicious contribution when the attacker controls both the code change and the accompanying PR text? We introduce SEVRA-BENCH (Social Engineering of Vulnerabilities in Review Agents), a benchmark that measures how often an automated reviewer
The increasing integration of LLMs into critical software development workflows, such as code review, necessitates robust security evaluations of their vulnerabilities to adversarial attacks.
A strategic reader should care because the exploitation of LLM-based code reviewers represents a novel and significant attack vector that could compromise software supply chain integrity and introduce systemic vulnerabilities.
The focus shifts from merely evaluating LLM code generation or vulnerability detection to understanding and mitigating their susceptibility to social engineering in review contexts.
- · Cybersecurity firms
- · DevSecOps tool vendors
- · Researchers in AI safety and security
- · Organizations relying solely on LLMs for code review without adversarial testing
- · Developers of insecure LLM-based review agents
- · Software supply chains vulnerable to social engineering
Automated code review agents are identified as a new frontier for social engineering attacks.
Increased investment and development of adversarial training and robust security measures for LLM-based review tools become imperative.
Introduction of new regulatory or industry standards for the deployment of AI in critical software development phases.
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Read at arXiv cs.AI