arXiv AI July 10, 2026 By Samuel Tetteh, Udip Shrestha, Joshua R. Waite, Cody Fleming

Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA

arXiv:2607. 08054v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, inside rigorous processes such as Systems-Theoretic Process Analysis (STPA).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

6d ago

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.

By Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang

llms agents reinforcement-learning safety

arXiv AI

Jul 28

Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation

arXiv:2607. 23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional rules of the form "A is required UNLESS B applies, UNLESS C overrides B".

By Paul Simpson, John Kozak, Lisa Doake

llms benchmarks safety

arXiv AI

6d ago

AI Guardrail Survival under Single-Cycle Agentic Self-Summarization

arXiv:2608. 11392v1 Announce Type: cross Abstract: Long-running agents periodically compact their context, replacing the transcript with a model-generated summary.

By Ted Kwartler, Alan Aqrawi, Arian Abbasi

llms agents nlp safety

arXiv AI

Jul 28

WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents

arXiv:2605. 27784v2 Announce Type: replace Abstract: LLM agents are governed by long-lived prompt policies, where individually reasonable stand- ing rules can jointly govern the same pre- generation state.

By Lu Yan, Xuan Chen, Xiangyu Zhang

llms agents

arXiv AI

Aug 12

Auditing Automated Evaluation, Error Propagation, and Runtime Mitigation in Tool-Using Language Agents

arXiv:2604. 16706v2 Announce Type: replace Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, yet this assumption is rarely validated against human annotation.

By Bhaskar Gurram

llms agents benchmarks

arXiv AI

Jun 2

When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems

arXiv:2606. 00448v1 Announce Type: cross Abstract: LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set.

By Su Wang, Pin Qian, Yihang Chen, Junxian You, Xiaoyuan Wang, Xiaochong Jiang, Lifei Liu, Haoran Yu, Jingzhou Xu

llms agents benchmarks safety

Related stories

Agent Safety Should Be a Runtime Contract

Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation

AI Guardrail Survival under Single-Cycle Agentic Self-Summarization

WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents

Auditing Automated Evaluation, Error Propagation, and Runtime Mitigation in Tool-Using Language Agents

When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems