Cheap to Hypothesize, Costly to Verify: The Defense Surface of Agentic Vulnerability Discovery
This paper formalises the economic asymmetry of LLM-driven vulnerability hunting — hypotheses are cheap but verification is costly — giving security leaders a framework to evaluate agentic scanning ROI realistically.
Summary written by editorial AI · Source link below
arXiv:2609.35909v1 Announce Type: new Abstract: Autonomous LLM agents turn vulnerability discovery into a repository-scale search: they generate many vulnerability hypotheses but can verify only a subset under a finite budget. We show that autonomous vulnerability discovery exhibits a hypothesis-verification asymmetry, where verifying a candidate hypothesis through reachability analysis, execution, and proof-of-concept construction is substantially more expensive than forming it. Under a finite
Editorial Analysis
As enterprises adopt AI agents for vulnerability discovery, understanding verification-budget constraints prevents over-reliance on unvalidated findings.
Establish verification-budget guardrails for any autonomous vulnerability-discovery tooling to ensure findings are confirmed before remediation resources are spent.
AI-driven vulnerability scanning produces many leads but verifying them is expensive — budget and process guardrails are essential.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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