This paper benchmarks LLMs for smart contract security analysis, concluding that while LLMs show potential, their reliability is limited by lexical bias and requires integration with traditional stati…

This paper proposes using large language models (LLMs) to generate and compositionally verify software implementations directly from natural language specifications, showing promising preliminary resu…

The paper introduces the Lean-Agent Protocol, a formal verification platform that uses Lean 4 theorem proving to ensure agentic AI actions in finance are mathematically compliant with complex regulati…

The paper proposes a trust-boundary architecture using Lean 4 to verify the deterministic structured computations surrounding LLM pipelines, providing verifiable certificates for high-stakes deploymen…

The paper introduces PSR extsuperscript{2}, a novel static analysis framework that significantly improves the detection of atomicity violations in smart contracts by combining structural path searchin…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…

The paper proposes a Semantic Gateway and a Zero-Trust security model to formally validate and secure autonomous AI agents operating in enterprise systems, achieving a 100% discovery rate of unauthori…

AutoVerifier is an LLM-based agentic framework that automates the end-to-end verification of complex technical claims, enabling non-experts to generate evidence-backed intelligence assessments.

The paper introduces KindHML, an automated formal verification approach that uses Hennessy-Milner Logic and the Kind 2 model checker to verify complex temporal properties of smart contracts, detecting…

QCIVET introduces a novel contract-based framework to ensure the integrity of hybrid quantum-classical pipelines by verifying both the structure (syntactic) and the behavior (semantic) of quantum stag…

The paper identifies a universal, statistically predictable distribution (Mandelbrot) governing LLM outputs, enabling a highly efficient, model-agnostic scoring primitive for provenance and quality as…

This paper introduces a novel framework, the Reasoning Safety Monitor, to detect and prevent logical inconsistencies and adversarial manipulations within the internal reasoning steps of large language…

The paper introduces TRAILS~, a novel method that improves code correctness validation by grounding LLM reasoning in concrete (input, output) pairs derived from specifications, achieving state-of-the-…

The paper introduces Behavioral Integrity Verification (BIV), a framework that systematically audits AI agent skills by comparing their declared capabilities against their actual implementation, revea…

The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reli…

LibScan is an automated framework that detects eight categories of smart contract library misuse by combining LLM-based semantic reasoning with rule-based analysis, achieving 85.15% accuracy on real-w…

RuleForge is an automated system that generates and validates detection rules for web vulnerabilities from structured CVE templates, significantly improving detection accuracy and reducing false posit…

COBALT-TLA introduces a neuro-symbolic verification loop that successfully and autonomously discovers novel cross-chain bridge vulnerabilities by integrating an LLM with the TLA+ model checker.

Pramana introduces a standardized, protocol-level wire format for autonomous agent outputs, ensuring that every consequential claim is accompanied by a verifiable artifact that can be re-executed by a…

The paper introduces an automated framework demonstrating that LLM system instructions are vulnerable to encoding attacks, where structured output requests can bypass safety refusals and leak sensitiv…