AI Threat AlertTrust in LLM-controlled Robotics: a Survey of Security Threats, Defenses and Challenges
taxonomy of attack vectors, covering topics such as jailbreaking, backdoor attacks, and multi-modal prompt injection. In response, we analyze and categorize a range of defense mechanisms, from formal safety
Sentra-Guard: A Multilingual Human-AI Framework for Real-Time Defense Against Adversarial LLM Jailbreaks
time modular defense system named Sentra-Guard. The system detects and mitigates jailbreak and prompt injection attacks targeting large language models (LLMs). The framework uses a hybrid architecture with FAISS
OpenSec: Measuring Incident Response Agent Calibration Under Adversarial Evidence
OpenSec, a dual-control reinforcement learning (RL) environment that evaluates IR agents under realistic prompt injection scenarios with execution-based scoring: time-to-first-containment (TTFC), evidence-gated action rate
MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
implanting malicious successful experiences into the agent's long-term memory. Unlike traditional prompt injections that are transient, or standard RAG poisoning that targets factual knowledge, MemoryGraft exploits the agent
Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents
integrated with a human-in-the-loop review process. Evaluations show our system reduces prompt-injection risk, blocks out-of-scope requests, and limits toxic outputs. It also generates auditable
The Shawshank Redemption of Embodied AI: Understanding and Benchmarking Indirect Environmental Jailbreaks
prompts to the embodied agent. In this paper, we propose, for the first time, indirect environmental jailbreak (IEJ), a novel attack to jailbreak embodied AI via indirect prompt injected into
Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
Thinking, and Phi-4, and across out-of-distribution benchmarks spanning harmful tasks, prompt injection, benign tool use, and cross-domain privacy leakage. MOSAIC reduces harmful behavior
API Secrets Should Never Become Tokens in the LLM's Vocabulary: A Threat Analysis of API Credential Handling in LLM Agent Systems and an Empirical Evaluation of a Vault-Mediated Execution Boundary
prompt or tool configuration, crosses from an authentication boundary into a data pipeline, where it may persist in conversation history, logs, memory stores, generated code, and error payloads. Prompt injection
Implicit Manipulation for Skill Selection in LLM Agents with Semantic Matching
should handle a user request. Existing attacks on this stage primarily rely on explicit prompt injection or instruction-level steering, which can expose recognizable manipulation signals. In this work
ContainmentBench: Trace-Based Evaluation of Post-Injection Containment in Tool-Using LLM Agents
process untrusted content, maintain memory, delegate across agents, and invoke side-effecting tools. Existing prompt-injection evaluations typically summarize security with terminal attack or policy outcomes, but equal endpoints
Practical and Stealthy Touch-Guided Jailbreak Attacks on Deployed Mobile Vision-Language Agents
safety alignment of LVLMs. Moreover, we developed three representative Android applications and curated a prompt-injection dataset for mobile agents. We evaluated our attack across multiple LVLM backends, including closed
SwitchCraft: Training-Free Multi-Event Video Generation with Attention Controls
training-free framework for multi-event video generation. Our key insight is that uniform prompt injection across time ignores the correspondence between events and frames. To this end, we introduce
SwitchCraft: Training-Free Multi-Event Video Generation with Attention Controls
training-free framework for multi-event video generation. Our key insight is that uniform prompt injection across time ignores the correspondence between events and frames. To this end, we introduce
SIR: Self-improving Red-teaming for Compute Use Agents
they can be exposed to untrusted content while operating, they are vulnerable to indirect prompt injection (IPI), in which an adversary plants instructions in content the agent will read
Detecting Malicious Agent Skills in the Wild using Attention
foothold, which turns the skill marketplace into a new attack surface for agentic systems. Prompt-injection defenses do not carry over to this setting. They rely on a boundary between
Reasoning Up the Instruction Ladder for Controllable Language Models
inputs and predefined higher-priority policies, our trained model enhances robustness against jailbreak and prompt injection attacks, providing up to a 20% reduction in attack success rate (ASR). These results
Surviving the Router: Optimizing Skill Injections for Retrieval and Execution
skill routers to execute complex tasks. While recent studies highlight the threat of prompt injections embedded in these skills, existing evaluations often assume settings where the malicious skill is already
Mission-Level Runtime Assurance for LLM-Assisted ISR Swarms over a Verification-Aware Fabric
than reported as mission-wide all-clears. On a simulated ISR mission, an indirect prompt injection that causes real LLM planners to split a prohibited collection task across four platforms
CHAI: Command Hijacking against embodied AI
this paper, we introduce CHAI (Command Hijacking against embodied AI), a physical environment indirect prompt injection attack that exploits the multimodal language interpretation abilities of AI models. CHAI embeds deceptive