arXiv AI
Jun 9
PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits
arXiv:2510. 17947v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are improving at an exceptional rate.
Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
arXiv:2602. 16346v4 Announce Type: replace-cross Abstract: LLM-based agents execute real-world workflows via tools and memory.
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arXiv Machine Learning
Jun 2
Investigating and Alleviating Harm Amplification in LLM Interactions
arXiv:2606. 02423v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their capabilities through extended interactions.
arXiv AI
Jul 22
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
arXiv:2606. 25476v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable performance across natural language processing tasks, yet their deployment in high-stakes applications raises critical concerns regarding reliability, safety, and trustworthiness.
arXiv AI
Aug 11
Automating Deception: Scalable Multi-Turn LLM Jailbreaks
arXiv:2511. 19517v3 Announce Type: replace-cross Abstract: Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves the way for a more significant one, to bypass safety alignments, pose a persistent threat to Large Language Models (LLMs).
arXiv AI
Aug 11
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories
arXiv:2604. 07223v2 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces.
arXiv AI
Aug 12
REDAgentBench: Executable Red Teaming and Faithful Measurement of LLM Agent Systems
arXiv:2608. 10669v1 Announce Type: new Abstract: Large language model (LLM) agents combine language-based reasoning with external tools to perform complex tasks.
arXiv AI
Jun 2
Constitutional Black-Box Monitoring for Scheming in LLM Agents
arXiv:2603. 00829v2 Announce Type: replace-cross Abstract: Safe deployment of Large Language Model (LLM) agents in autonomous settings requires reliable oversight mechanisms.
arXiv AI
Jul 22
Operational Hallucination and Safety Drift in AI Agents
arXiv:2607. 18366v1 Announce Type: new Abstract: Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution.
arXiv AI
Jul 3
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs
arXiv:2507. 22063v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) for code generation (i.
arXiv AI
Jun 19
Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems
arXiv:2606. 20470v1 Announce Type: cross Abstract: Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents.
arXiv AI
Jul 9
Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents
arXiv:2607. 07474v1 Announce Type: cross Abstract: Agentic red-teaming benchmarks report whether an injected agent was compromised as a single bit: the attack succeeded, or it did not.
arXiv AI
Jun 19
The Autonomy Tax: Defense Training Breaks LLM Agents
arXiv:2603. 19423v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously complete complex multi-step tasks.