arXiv AI
Jun 24
Red-Teaming the Agentic Red-Team
arXiv:2606. 24496v1 Announce Type: cross Abstract: The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability.
Proof-of-Guardrail in AI Agents and What (Not) to Trust from It
arXiv:2603. 05786v2 Announce Type: replace-cross Abstract: As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised.
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Hugging Face Trending Papers
Jun 23
Red-Teaming the Agentic Red-Team
The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However, while the community has focused on creating more and more capable agents, less attention has been allocated to assessing the security of those systems.
arXiv AI
Jun 4
Grimlock: Guarding High-Agency Systems with eBPF and Attested Channels
arXiv:2605. 27488v2 Announce Type: replace-cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds.
arXiv AI
Jul 7
Don't Make Models Guess Security and Safety: Symbolic Guardrails for Domain-Specific AI Agents
arXiv:2604. 15579v2 Announce Type: replace-cross Abstract: There is increasing interest in integrating AI agents that invoke tools into domain-specific commercial software, where unintended tool calls can cause serious security and safety incidents.
arXiv AI
Jul 23
Rewriting the Response Path: Silent Tampering and Provider-Signed Defense in BYOK LLM Agents
arXiv:2605. 02187v2 Announce Type: replace-cross Abstract: LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions.
arXiv Machine Learning
Jul 23
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
arXiv:2603. 07466v2 Announce Type: replace-cross Abstract: Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs).
arXiv AI
3d ago
Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agents
arXiv:2609.39065v1 Announce Type: cross Abstract: LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabi...
arXiv AI
Jun 2
Benchmarking Security Risk Detection and Verification in Open Agentic Skill Ecosystems
arXiv:2606. 00925v1 Announce Type: cross Abstract: Open agent platforms allow community contributors to publish reusable skills that agents can invoke at runtime.
arXiv AI
Jun 11
Sovereign Assurance Boundary: Certificate-Bound Admission for Agentic Infrastructure
arXiv:2606. 11632v1 Announce Type: cross Abstract: Agentic infrastructure introduces a critical control-plane authorization problem: non-deterministic reasoning systems can propose high-stakes mutations to production resources, yet existing security mechanisms -- such as identity and access management (IAM), policy engines, consensus protocols, and audit logs -- either enforce static, context-unaware permissions or merely record actions post-execution.
arXiv AI
Jun 24
Cryptographic certificates of validity for trustworthy AI
arXiv:2606. 23768v1 Announce Type: cross Abstract: We propose cryptographic certificates of validity for agentic AI systems.
arXiv AI
Sep 17
Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks
The paper revisits Thompson’s classic compiler back‑door attack in the context of self‑modifying AI coding agents. By poisoning the benchmarks used for self‑evaluation, the authors demonstrate that agents such as the Darwin Gödel Machine, Self‑Improving Coding Agent, and Hyperagents can be coaxed into generating vulnerable code, even on clean, held‑out tasks. Experiments show that the contamination can persist after subsequent clean training, highlighting the need for more robust agent designs.
arXiv AI
Jul 1
Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens
arXiv:2606. 30755v1 Announce Type: cross Abstract: Claw-like AI agents (e.