AI Threat Alert
Salience Induction against Multi-Hop RAG Agents: Threat and Defense
facts across documents. Existing defenses focus on content poisoning, which injects false facts, and prompt injection, which embeds directives. We identify a third attack surface: the salience channel, through which
Beyond Wireless Security: Covert Communications in Large Language Model-enabled Edge Networks
computations make LLMENs susceptible to various security threats, such as eavesdropping, jamming, prompt poisoning, and prompt injection attacks. Since existing countermeasures against these attacks often incur prohibitive overhead, developing holistic
Fortytwo: Swarm Inference with Peer-Ranked Consensus
evaluation indicates higher accuracy and strong resilience to adversarial and noisy free-form prompting (e.g., prompt-injection degradation of only 0.12% versus 6.20% for a monolithic single-model baseline), while
Agentic Cloud Decoys: A Deception-Driven Framework for Autonomous Intrusion Investigation
attacker chosen values providers record verbatim, which makes any log to prompt path an indirect prompt injection channel that a decoy widens rather than narrows. We address the first
SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents
agent security with 600 controlled adversarial tasks across six attack families: direct and indirect prompt injection, tool-return injection, memory poisoning, memory extraction, and ambiguity-driven unsafe inference. SafeClawBench reports
CIBER: A Comprehensive Benchmark for Security Evaluation of Code Interpreter Agents
vulnerability of code interpreter agents against four major types of adversarial attacks: Direct/Indirect Prompt Injection, Memory Poisoning, and Prompt-based Backdoor. We evaluate six foundation models across two representative code
Boundary-Aware Test-Time Adaptation for Zero-Shot Medical Image Segmentation
test-time adaptation. This framework integrates two key mechanisms: (1) The encoder-level Gaussian prompt injection embeds Gaussian-based prompts directly into the image encoder, providing explicit guidance for initial
Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models
Prompt attacks, including jailbreaks and prompt injections, pose a critical security risk to Large Language Model (LLM) systems. In production, guardrails must mitigate these attacks under strict low-latency constraints
MIRROR: Novelty-Constrained Memory-Guided MCTS Red-Teaming for Agentic RAG
Multimodal agentic retrieval-augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image injection, direct-query attacks, and orchestrator-level tool manipulation. Existing
Paraphrasing Adversarial Attack on LLM-as-a-Reviewer
growing attention, making it essential to examine their potential vulnerabilities. Prior attacks rely on prompt injection, which alters manuscript content and conflates injection susceptibility with evaluation robustness. We propose
What Matters For Safety Alignment?
services, highlighting an urgent need for architectural and deployment safeguards. Fourth, roleplay, prompt injection, and gradient-based search for adversarial prompts are the predominant methodologies for eliciting unaligned behaviors
PromptScreen: Efficient Jailbreak Mitigation Using Semantic Linear Classification in a Multi-Staged Pipeline
Prompt injection and jailbreaking attacks pose persistent security challenges to large language model (LLM)-based systems. We present PromptScreen, an efficient and systematically evaluated defense architecture that mitigates these threats
Penetration Testing of Agentic AI: A Comparative Security Analysis Across Models and Frameworks
functionality of a university information management system and 13 distinct attack scenarios that span prompt injection, Server Side Request Forgery (SSRF), SQL injection, and tool misuse. Our 130 total test
GhostEI-Bench: Do Mobile Agents Resilience to Environmental Injection in Dynamic On-Device Environments?
inter-app interactions, exposes them to a unique and underexplored threat vector: environmental injection. Unlike prompt-based attacks that manipulate textual instructions, environmental injection corrupts an agent's visual perception
Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance
stealthy attack vector that embeds adversarial operational narratives into bootstrap guidance files. Unlike traditional prompt injection, which relies on explicit malicious instructions, guidance injection manipulates the agent's reasoning context
The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code
integration. We produce payloads in two settings: using prompts that specify only functional requirements, and using prompts that inject a structured history of prior outcomes to force divergence. We measure
The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code
integration. We produce payloads in two settings: using prompts that specify only functional requirements, and using prompts that inject a structured history of prior outcomes to force divergence. We measure
Agent Data Injection Attacks are Realistic Threats to AI Agents
user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security has primarily focused on indirect prompt injection (IPI). Its most well
Imperceptible Jailbreaking against Large Language Models
imperceptible jailbreaks achieve high attack success rates against four aligned LLMs and generalize to prompt injection attacks, all without producing any visible modifications in the written prompt. Our code
Verifying Intent and Harm: A Unified Defense Against LLM-Generated Threats
conflict resolution. We formalize a threat model for prompt-response attacks and evaluate the framework across five threat categories: jailbreaks, prompt injection, phishing, cyber abuse, and harmful content. Experiments