AI Threat Alert
LoopTrap: Termination Poisoning Attacks on LLM Agents
this self-directed loop facilitates autonomy, it also introduces a critical risk: by injecting malicious prompts into the agent's context, an adversary can distort the agent's termination judgment
PraisonAI: Jobs API exposes agent-execution endpoints with no authentication
Targeting World Models to Compromise Robot Learning Pipelines
directly implant dangerous trajectories into sold or uploaded datasets, our novel attack methods inject malicious prompts or compromising transition dynamics into visibly safe teleoperated datasets which are only activated once
LAAF: Logic-layer Automated Attack Framework A Systematic Red-Teaming Methodology for LPCI Vulnerabilities in Agentic Large Language Model Systems
pipelines, and external tool connectors face a class of attacks - Logic-layer Prompt Control Injection (LPCI) - for which no automated red-teaming instrument existed. We present LAAF (Logic-layer Automated
Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw
OpenClaw ecosystem. We systematically investigate its current threat landscape, highlighting critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain
Biosecurity-Aware AI: Agentic Risk Auditing of Soft Prompt Attacks on ESM-Based Variant Predictors
GFMs. SAGE functions through an interpretable and automated risk auditing loop. It injects soft prompt perturbations, monitors model behavior across training checkpoints, computes risk metrics such as AUROC and AUPR
DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents
propose DTap-Red, the first autonomous red-teaming agent that systematically explores diverse injection vectors (e.g., prompt, tool, skill, environment, combinations) and autonomously discovers effective attack strategies tailored to varying
Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling Autonomous LLM Systems
detection under stealthy strategies, and (iii) resilience of verifiability mechanisms to adversarial prompt and persona injection. Our approach shifts the evaluation focus from how likely misalignment is to how quickly
AULLM++: Structural Reasoning with Large Language Models for Micro-Expression Recognition
propose AULLM++, a reasoning-oriented framework leveraging Large Language Models (LLMs), which injects visual features into textual prompts as actionable semantic premises to guide inference. It formulates AU prediction into
Structural Role Injection in Handlebars-Templated LLM Prompts: Triple-Brace Interpolation, Delimiter Family, and the Limits of HTML Auto-Escaping
Large language model applications build prompts from templates, and Handlebars is a widely used templating engine and the default prompt-template format in Microsoft Semantic Kernel. Its double-brace
BadTemplate: A Training-Free Backdoor Attack via Chat Template Against Large Language Models
chat templates allows an attacker who controls the template to inject arbitrary strings into the system prompt without the user's notice. Building on this, we propose a training-free
Stealthy Backdoor Attacks against LLMs Based on Natural Style Triggers
attack in a realistic threat model and systematically evaluate BadStyle under both prompt-induced and PEFT-based injection strategies. Extensive experiments across seven victim LLMs, including LLaMA, Phi, DeepSeek
Black-Box Skill Stealing Attack from Proprietary LLM Agents: An Empirical Study
prior prompt-stealing methods and build an automated stealing prompt generation agent. This agent starts from model-generated seed prompts, expands them through scenario rationalization and structure injection, and enforces
Multi-Turn Jailbreaking of Aligned LLMs via Lexical Anchor Tree Search
injection. LATS reformulates jailbreaking as a breadth-first tree search over multi-turn dialogues, where each node incrementally injects missing content words from the attack goal into benign prompts. Evaluations
CoT-Guard: Small Models for Strong Monitoring
attacks, where the adversary is a third-party LLM router injecting hidden objectives into code-generation requests through either prompt manipulation or code manipulation attacks. To push beyond objectives that
files, which leads to a server side template injection vulnerability within langchaingo, allowing an attacker to insert a statement into a prompt to read the "etc/passwd" file
Generate "Normal", Edit Poisoned: Branding Injection via Hint Embedding in Image Editing
rendered onto semantically related objects, even when the user prompt does not explicitly mention it. This form of hidden payload injection makes the attack stealthy. We study two realistic attack
Synthetic Voices, Real Threats: Evaluating Large Text-to-Speech Models in Generating Harmful Audio
second leverages audio-modality exploits (Read, Spell, Phoneme) that inject harmful content through auxiliary audio channels while maintaining benign textual prompts. Through evaluation across five commercial LALMs-based TTS systems
Evaluating Adversarial Vulnerabilities in Modern Large Language Models
prompted to circumvent their own safety protocols, and 'cross-bypass', where one model generated adversarial prompts to exploit vulnerabilities in the other. Four attack methods were employed - direct injection, role