arXiv AI
MASCRDM: Multi-Agent System for Compliance Risk Detection and Mitigation in Training Process of Large Language Models
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arXiv AI
Jul 7
AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents
arXiv:2607. 02599v1 Announce Type: cross Abstract: Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges.
arXiv AI
Jun 24
AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
arXiv:2606. 24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments.
arXiv AI
Sep 23
Trustworthy Agentic AI: Failure Modes, Mitigation Strategies, and a Lifecycle Framework for Autonomous LLM Systems
The paper discusses the trustworthiness of agentic AI systems built on large language models, highlighting new security and operational risks such as indirect prompt injection, memory contamination, and cross‑session data leakage. It categorizes failure modes, reviews mitigation strategies—including instruction hierarchies, context isolation, and constrained tool use—and introduces the Trustworthy Agent Development Lifecycle (TADL), a six‑phase framework for specification, design, training, evaluation, deployment, and monitoring. The authors note that TADL has not yet been empirically validated but offers a structured foundation for developing more secure and accountable agentic systems, and they call for improved benchmarks and future research priorities.
arXiv Computation and Language
Sep 23
SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking
arXiv:2508.15526v2 Announce Type: replace Abstract: The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabili...
arXiv AI
Aug 5
Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
arXiv:2606. 02965v2 Announce Type: replace Abstract: As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion.
arXiv AI
Aug 18
Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
arXiv:2608. 14590v1 Announce Type: new Abstract: LLM agents increasingly perform irreversible real-world actions, including database updates, API calls, file operations, and autonomous use of tools.