arXiv AI
Jun 18
Code-Augur: Agentic Vulnerability Detection via Specification Inference
arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.
From Capability to Assurance in Autonomous Penetration-Testing Harnesses: A Framework and Reference Implementation
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arXiv AI
Jun 4
From Agent Traces to Trust: Evidence Tracing and Execution Provenance in LLM Agents
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.
arXiv AI
Jul 3
Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
arXiv:2607. 01793v1 Announce Type: new Abstract: LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks.
arXiv AI
4d ago
When the Agent Becomes the Kernel: A Systematization of Security on the Path to AI-Native Operating Systems
The paper discusses how large language model agents now act as privileged principals with kernel‑grade authority, yet lack the trusted mediation traditionally required for operating‑system security. It introduces a taxonomy that distinguishes between provenance‑based deterministic checks and content‑semantic checks, identifying a central mediation gap in distinguishing data from instruction and authorized from unauthorized actions. The authors argue that this gap creates an irreducible risk of undetected attacks whenever inputs and actions are not pre‑enumerated, and they propose defenses across runtime monitoring, architectural separation, and authorization while critiquing current evaluation practices. They extend the analysis to AI‑native operating systems where the model itself serves as the arbitration core, outlining design constraints, challenges, and a research agenda.
arXiv AI
Jul 29
Explanation-Bound Tool Execution for AI Agents: Server-Verified Action Claims Without Trusting Model Rationales
arXiv:2607. 25364v1 Announce Type: new Abstract: Tool-using agents expose structured calls but commonly attach free-form rationales.
arXiv AI
2d ago
Calibrated Decision Models for Autonomous Penetration-Testing Harnesses: JEV and Laya as System One Decision Layers for LLM-Driven Pentest Agents
The paper proposes using lightweight, calibrated System One decision models—specifically JEV and Laya—to improve autonomous penetration-testing harnesses that rely on large language models (LLMs). It defines four key decision points (finding adjudication, severity recalibration, agent pruning, and confirmation loops) and presents a NeuroSploit case study showing differences in severity distribution, runtime, and grading when using TypeSafe System One. The authors review existing System One specifications, discuss various RL-based training approaches, and introduce Rave, a domain‑adapted model with a proposed training and evaluation framework.
arXiv AI
Jul 8
Proof of Execution: Runtime Verification for Governed AI Agent Actions
arXiv:2607. 05397v1 Announce Type: cross Abstract: Agent systems increasingly execute rather than advise.
arXiv AI
Aug 28
How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation
The paper introduces CTF-ABACUS, a trace-based auditing framework that reconstructs each autonomous language-model agent’s run in Capture-the-Flag (CTF) challenges into evidence‑grounded solve profiles. By decomposing actions into penetration‑testing phases and techniques, it distinguishes genuine exploitation from shortcut methods such as memorized recall or guessing. Applying the framework to 1,435 CTF attempts by six models on 240 challenges shows that only 62‑87% of recovered flags are trace‑verified, highlighting that many successes rely on shallow trajectories rather than true exploitation.
arXiv AI
Jun 12
The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
arXiv AI
Jun 16
From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
arXiv:2606. 04990v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents are evolving from passive text generators into autonomous systems capable of planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.
arXiv AI
Sep 12
Beyond Static Guarantees: Measuring the Static-Pass Dynamic-Fail Gap in Security-Sensitive and LLM-Generated Python Code
The paper introduces the Static‑Pass Dynamic‑Fail (SPDF) phenomenon, showing that static analysis can miss vulnerabilities that are exploitable at runtime. Using a three‑stage pipeline—static scanning, LLM‑driven CWE reasoning, and autonomous exploit verification—it evaluated 1,355 Python samples and found that 14.53% of samples that passed static checks were actually exploitable. The study highlights that static‑analysis success and runtime security are distinct assurance layers, especially for AI‑generated and security‑sensitive code.
arXiv AI
Aug 13
Agent Safety Should Be a Runtime Contract
arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.