EntailLLM is a framework that verifies vulnerability discovery paths generated by large language model agents using a separate knowledge graph and logic programming. The system checks whether the agent’s traversal of a function call graph is entailed by domain knowledge, improving pooled entailment from 77% to 98% across various binaries and CWE classes. It is deployed on real medical-device binaries, achieving high entailment without per-device tuning and offering logical, explainable verification grounded in formal semantics.
arXiv:2609.15523v1 Announce Type: cross Abstract: Logic attack graphs grounded in scanner output provide explicit and auditable attack path reasoning LLM-based agents lack. Integrating symbolic frame...
arXiv:2604.10800v2 Announce Type: replace-cross Abstract: Learned classifiers deployed in agentic pipelines face a fundamental reliability problem: predictions are probabilistic inferences, not verif...
The paper examines how Large Language Models (LLMs) have introduced a trustworthiness crisis in cybersecurity vulnerability assessment, producing artifacts known as "AI slop" such as hallucinated vulnerabilities and incorrect patches. It presents a taxonomy of these failures, attributes them to a mismatch between deductive reasoning and probabilistic generation, and introduces the Deductive Coverage Score as a measurable proxy. The authors review mitigation strategies, critique passive approaches, and propose active neuro‑symbolic verification, concluding with new evaluation tools—CVE‑Bench and Slop‑Score—to shift focus from linguistic fluency to mathematical verifiability.
arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.
MetaVul is a self‑improving framework that discovers and refines harnesses—code scaffolding around large language model agents—for vulnerability detection. Starting from a seed reasoning scaffold, it iteratively optimizes harnesses using feedback from paired vulnerable and patched functions, producing Pareto‑optimal designs that can be reused across different models. Experiments show that optimized harnesses can boost pairwise vulnerability detection by up to 25.8 percentage points and transfer effectively between models without further tuning.
The paper introduces Trustworthy RAG, an evaluation agent designed to detect misinformation and knowledge poisoning in Retrieval-Augmented Generation systems. It combines natural language inference verification, a five-signal poison detector, and a weighted Trust Index to assess the reliability of retrieved content. Experiments on multiple LLMs show high accuracy and precision, with the agent effectively blocking unsafe advice in a secure-coding assistant scenario.
The paper introduces VERA, an automated framework that audits large language model (LLM) reasoning in software vulnerability analysis. Instead of relying on free‑form explanations, VERA requires models to produce a Structured Reasoning Record (SRR) that captures pointers, memory operations, and state transitions in machine‑readable fields. A multi‑stage judge then checks each SRR against eight reasoning failure modes, revealing that reasoning flaws are as common in correct verdicts as in incorrect ones and that VERA detects 87% of errors missed by free‑form LLM‑as‑judge evaluations.
arXiv:2606. 03453v1 Announce Type: cross Abstract: Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation.
Safin-1.5 is a large language model that incorporates a safety‑native architecture and a recursive safety improvement loop to enhance its own safety while maintaining utility. The architecture uses persistent safety states and hazardous‑knowledge disentanglement to isolate and remove risky knowledge with minimal impact on general capabilities. An inference‑time harness extends the model’s abilities to formal reasoning by combining language understanding with symbolic inference and verification, enabling the construction of checkable proofs.
arXiv:2607. 12723v1 Announce Type: cross Abstract: Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities.
arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.
arXiv:2606. 19149v1 Announce Type: cross Abstract: Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs.