Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
arXiv:2607.05842v1 Announce Type: cross Abstract: Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associated with misuse. Existing safety and cybersecurity evaluations are difficult to interpret in this setting because they often compare unrelated model families, thereby conflating safety behavior with differences in architecture, scale, training data, and deployment. To isolate this factor, we study safety state: whether refusal
The rapid deployment of LLMs into critical applications like software security necessitates immediate research into their vulnerabilities and safety mechanisms.
This research addresses a critical gap in understanding how LLMs perform vulnerability analysis, especially when safety measures might inadvertently block legitimate security functions.
Understanding safety behaviors across different model families will allow for more targeted development of secure and effective LLM-assisted security tools.
- · Cybersecurity sector
- · Software developers
- · AI safety researchers
- · Unsecured software systems
- · Developers deploying un-audited LLMs
Improved clarity on LLM 'refusal' behavior for cybersecurity tasks leading to more reliable AI-assisted security tools.
Development of specialized LLMs or fine-tuning techniques balanced for both safety and effective vulnerability analysis.
Enhanced overall software supply chain security due to more robust and less 'conflicted' AI security agents.
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Read at arXiv cs.AI