What an agent can be trusted to establish is bounded by the strength of its feedback, and the central lesson is what an agent can be trusted to establish is bounded by the strength of its feedback.
Abstract
AI coding agents produce code faster than humans can review it. In our approach, the prover is the judge of whether the code is correct. Under a verifier-driven loop, AI agents wrote and verified bare-metal security software in Ada/SPARK spanning classical and post-quantum cryptography, TLS 1.3, IKEv2, X.509, and a Matrix client. GNATprove discharged 49,280 proof obligations, established functional correctness for selected primitives, and proved the absence of run-time errors for the rest, at roughly 20-40 times lower supervision cost than comparable hand verification. GNATprove alone was insufficient: some defects could not be detected and were resolved using known-answer tests, interoperability, or human review of specifications. Given weak checks, the agent tried to bypass them and reported success. We report where each layer caught faults and draw the central lesson: what an agent can be trusted to establish is bounded by the strength of its feedback.
This paper evaluated Niyam-AI on 2,000 real-world agent scenarios from Agent-SafetyBench and compared it against three existing safety approaches: NeMo Guardrails, Meta's Llama Prompt Guard 2, and OpenAI's GPT-OSS-Safeguard.
Preliminary evidence is found that a single sentence added to the developer's own prompt substantially improves disclosure, and a benchmark scored on attack success alone cannot rank agents on the risk a developer actually carries.
Aaron Su, Rong-Xing Lu· Inquiry@Queen's Undergraduat...· 0 citations
NiyamAI is presented, an intent bound runtime guardrail architecture providing cryptographically verifiable execution integrity for autonomous agents and subjects its own enforcement mechanism to 18 adversarial vectors across six classes, disclosing two implementation vulnerabilities identified and remediated during de...
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Security-adjusted reliability@k is proposed, which counts only rollouts that are both functionally correct and free of high-severity insecure patterns, which counts only rollouts that are both functionally correct and free of high-severity insecure patterns.
Coding agents need to establish that a program satisfies a specification and that the specification captures the requested behavior. We study how expert diagnosis of verification failures can become reusable guidance for this work. Our approach combines executable language definitions in the K framework with a kit of p...
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Large language models (LLMs) have shown strong potential for assisting software and security analysis tasks, yet their effectiveness in cryptographic symbolic protocol verification remains insufficiently understood. In this paper, we conduct the first systematic evaluation of the capability of state-of-the-art LLMs in...
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