AI Threat Alert
Protecting Context and Prompts: Deterministic Security for Non-Deterministic AI
Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot prevent. We introduce two novel primitives--authenticated prompts and authenticated context
Efficient and Adaptable Detection of Malicious LLM Prompts via Bootstrap Aggregation
However, these systems remain susceptible to malicious prompts that induce unsafe or policy-violating behavior through harmful requests, jailbreak techniques, and prompt injection attacks. Existing defenses face fundamental limitations: black
VIGIL: Defending LLM Agents Against Tool Stream Injection via Verify-Before-Commit
agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated metadata and runtime feedback hijack execution flow. Existing defenses encounter
From Secure Agentic AI to Secure Agentic Web: Challenges, Threats, and Future Directions
Secure Agentic Web. We first summarize a component-aligned threat taxonomy covering prompt abuse, environment injection, memory attacks, toolchain abuse, model tampering, and agent network attacks. We then review defense
Soft Instruction De-escalation Defense
agentic systems that interact with an external environment; this makes them susceptible to prompt injections when dealing with untrusted data. To overcome this limitation, we propose SIC (Soft Instruction Control
When Skills Lie: Hidden-Comment Injection in LLM Agents
Skills to describe available tools and recommended procedures. We study a hidden-comment prompt injection risk in this documentation layer: when a Markdown Skill is rendered to HTML, HTML comment
Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem
taxonomy of risks in the MCP ecosystem, distinguishing between adversarial security threats (e.g., indirect prompt injection, tool poisoning) and epistemic safety hazards (e.g., alignment failures in distributed tool delegation
MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents
handling. MSB contributes: (1) a taxonomy of 12 attacks including name-collision, preference manipulation, prompt injections embedded in tool descriptions, out-of-scope parameter requests, user-impersonating responses, false-error
Trust 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
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
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
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