arXiv:2604.11950v2 Announce Type: replace-cross Abstract: While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of automated bug detection. We frame this challenge as a test generation task: given a candidate report, synthesizing an executable proof-of-concept (PoC) - such as a script, command sequence, or crafted input - to trigger the suspected defect. Automated PoC generation can act as a scalable validation oracle, enabling end-to-end autonomous bug detection by providi
The rapid advancement and integration of large language models (LLMs) into software development processes necessitate more robust and autonomous bug detection and validation mechanisms.
This development addresses a critical bottleneck in the practical application of AI for software security and quality assurance, moving LLM bug detection from static reports to executable proof-of-concepts.
The ability to automatically generate executable Proof-of-Concepts from LLM bug reports could significantly elevate the scalability and autonomy of software vulnerability detection and remediation.
- · Cybersecurity sector
- · Software development companies
- · AI agent developers
- · Cloud infrastructure providers
- · Manual software testers
- · Traditional static analysis tools
- · Cyber adversaries (in the short term)
Reduced time and cost associated with identifying and validating software vulnerabilities, leading to more secure and reliable applications.
Increased adoption of LLM-based tools for end-to-end software development and security, potentially leading to new industry standards.
A potential shift in the skillset required for software security professionals, emphasizing agent oversight and complex vulnerability analysis over manual PoC generation.
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