arXiv AI
Jun 10
BadRobot: Jailbreaking Embodied LLM Agents in the Physical World
arXiv:2407. 20242v5 Announce Type: replace-cross Abstract: Embodied AI represents systems where AI is integrated into physical entities.
Easier Said Than Done: Unpacking Intent-Behavior Gap in Jailbreaking LLM-Based Robots
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arXiv AI
Jun 17
Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
arXiv:2510. 01359v2 Announce Type: replace-cross Abstract: Code-capable large language model (LLM) agents are embedded in software engineering workflows where they can read, write, and execute code, raising "jailbreak" stakes beyond text-only settings.
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
Jun 15
From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails
arXiv:2606. 14517v1 Announce Type: cross Abstract: LLM-based guardrails have emerged as a highly effective defense against prompt injection and jailbreak attacks in autonomous agents.
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 Computation and Language
Aug 27
A Self-Evolving Multi-Agent Framework Defense against LLM Jailbreak Attacks
The paper introduces a self‑evolving defense framework for large language models that uses a persistent, cross‑interaction rule memory to adapt to new jailbreak attacks. When an attack succeeds, the system abstracts the failure into a method‑level rule that captures the structural attack wrapper, allowing the rule to generalize across an entire attack family. This memory‑based adaptation operates without parameter updates, works with both open‑weight and black‑box models, and has been shown to reduce attack success rates while preserving benign utility across multiple jailbreak families.
arXiv AI
Jun 24
MuTRAP: Multi-trigger Trojans Attacking Robot Task Planning Systems
arXiv:2504. 17070v3 Announce Type: replace-cross Abstract: Robots need task planning methods to achieve goals that require more than one action.
arXiv AI
Sep 1
Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
arXiv:2604.20994v2 Announce Type: replace-cross Abstract: The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the c...
arXiv AI
Sep 1
SIR: Self-improving Red-teaming for Compute Use Agents
arXiv:2608.30207v1 Announce Type: cross Abstract: Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal...
arXiv AI
Aug 20
Jailbreaking in the Haystack
The paper "Jailbreaking in the Haystack" introduces NINJA, a jailbreak technique that exploits long-context language models by appending benign, model-generated content to harmful user goals. It demonstrates that the position of harmful goals within the context is crucial for safety, and shows that NINJA significantly boosts attack success rates on models such as LLaMA, Qwen, Mistral, and Gemini. Unlike previous methods, NINJA is low-resource, transferable, less detectable, and compute‑optimal, revealing that carefully crafted benign long contexts can expose fundamental vulnerabilities in modern LMs.
arXiv AI
Sep 10
SRD-GUARD: A Defense Framework of LLMs via Semantic Rewriting and Joint Multi-Model Scoring for Latent Intent Exposure
arXiv:2609.06540v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical applications, yet jailbreak attacks can conceal harmful intent through role...
arXiv AI
Aug 11
When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Output
arXiv:2503. 24191v4 Announce Type: replace-cross Abstract: Content Warning: This paper may contain unsafe or harmful content generated by LLMs that may be offensive to readers.