PraisonAI: Arbitrary File Read via `@file:` Mention Path Traversal

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

Text Prompter – Unlimited chatgpt text prompts for openai tasks plugin for WordPress is vulnerable to Stored Cross-Site Scripting via the plugin's 'text_prompter' shortcode in all versions

llama-cpp-python is the Python bindings for llama.cpp. `llama

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

Flowise: Parameter Override Bypass Remote Command Execution

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

Open WebUI: Cross-user file disclosure via /api/chat/completions image_url

vLLM Vulnerable to Remote DoS via Special-Token Placeholders

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

MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents

induction that leverages adversarial perturbations and stealthy OCR injection to override the original user objective. Unlike prior attacks that operate on prompts or text-only memory, our approach enables persistent

LiteLLM: Server-Side Template Injection in /prompts/test endpoint

Large Language Models for Detecting Cyberattacks on Smart Grid Protective Relays

perfect fault detection accuracy. Additional evaluations demonstrate robustness to prompt formulation variations, resilience under combined time-synchronization and false-data injection attacks, and stable performance under realistic measurement noise levels