ProofSec: Benchmarking Epistemic Robustness and Evidence-Grounded Vulnerability Reasoning in Frontier LLM
This is a submission for the Kaggle Benchmarking Challenge What happens when an LLM recognizes every lexical and semantic signature associated with a vulnerability - IDOR, BOLA, authorization bypass, predictable identifiers - but the available evidence does not actually establish that the vulnerability exists? That question is the foundation of ProofSec , an evidence-centric security reasoning…
The ProofSec benchmark assesses the ability of large language models to reason about security evidence and draw valid conclusions. It focuses on distinguishing between recognizing vulnerability indicators and evidencing that a vulnerability actually exists. The benchmark presents scenarios where an LLM must classify whether a security state is vulnerable, not vulnerable, or if there is insufficient evidence.
Traditional binary classification is insufficient as real-world investigations often involve incomplete, ambiguous, or contradictory observations. ProofSec evaluates whether an LLM can recognize when evidence is insufficient to establish a security claim. It introduces concepts like evidentiary sufficiency, causal relevance, authorization reasoning, and uncertainty management.
The benchmark is designed to push language models to demonstrate robustness to adversarial framing, controlled hypothesis revision, and evidence-grounded classification. By isolating different components of the benchmark such as dataset, prompt construction, model inference, structured parsing, assertion, and scoring, it enables precise evaluation of each component's performance.
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