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AI-Assisted Vulnerability Hunting is Here

LLM-assisted cybersecurity research is here and making real contributions. But, as Aisle notes, there are questions of how this affects the ability of defenders to ingest and act on vulnerability reports, as well as broader questions of whether adversaries will also use here capabilities to become more capable.

Aisle’s recent blog, “What AI Security Research Looks Like When It Works,” does a nice job in explaining the utility of LLM-enabled security research. Properly scoped and resourced, researchers can identify serious vulnerabilities that make communities much safer after patches are applied.

However, there is a distinction between high-quality reports and slop-quality reports. Some groups, such as those operating open source projects, are seeing increasing amounts of low-quality reports that are overwhelming their ability to triage incoming reports.

Aisle highlights several emergent challenges associated with LLM-enabled security research:

  1. If vulnerability reporting increases while maintainer numbers remain flat, there is a question of whether this will cause burnout among maintainers and thus impair both security- and feature-related development.
  2. Whether the 90-day responsible disclosure window remains appropriate, or needs to be tightened, in the current era of LLM-assisted discovery. At the same time, how can or should vulnerability reports be deduplicated?
  3. Whether the ability to identify and patch vulnerabilities will ultimately favour defenders or attackers.
  4. The community’s response to a substantial shift in vulnerability discovery remains uncertain.

There are a few other considerations not taken up in Aisle’s blog:

  1. To what extent will the increased ability of attackers to find vulnerabilities shift who is identified as an ‘advanced’ threat actor? While persistence is currently still linked to resourcing to maintain operations, if serious vulnerabilities (and their chains) become more widely discoverable, what effect will this have on a broader subset of actors being able to conduct cyber operations?
  2. In what ways will the organizations producing foundational models need to build in user identity or verification functionalities or access controls to potentially restrict who can (and cannot) use the models to undertake cybersecurity research?
  3. What might occur if adversaries attempt to poison training data or model weights in order to impede specific forms of LLM-enabled cybersecurity research, either now or in the future?

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