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

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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

Knowledge Base Poisoning Attacks and Defense for Policy-Aware LLM-RAG Framework

operator query types without requiring knowledge of runtime prompts. The attack achieves 85% LLM context corruption from a single injected rule (1.6% poisoning rate) and saturates at 7.7% poisoning, demonstrating

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

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

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

Exposing Citation Vulnerabilities in Generative Engines

perspectives of citation publishers and the content-injection barrier, defined as the difficulty for attackers to manipulate answers to user prompts by placing malicious content on the web. GEs integrate

Cross-Cutting Security Analysis of LLM-Generated Code via Metamorphic Testing and Association Rule Mining

level risk factors. We define nine MRs covering major CWE categories, including SQL injection, XSS, command injection, path traversal, hard-coded credentials, weak cryptography, and memory-safety errors, and apply

Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections

that memory evolution can convert one-time indirect injection into persistent compromise, which suggests that defenses focused only on per-session prompt filtering are not sufficient for self-evolving agents

Sockpuppetting: Jailbreaking LLMs Without Optimization Through Output Prefix Injection

assistant message block rather than the user prompt, increasing ASR by 64% over GCG on Llama-3.1-8B in a prompt-agnostic setting. The results establish sockpuppetting

Automating Agent Hijacking via Structural Template Injection

ecosystem, enables adversaries to manipulate execution by injecting malicious instructions into retrieved content. Most existing attacks rely on manually crafted, semantics-driven prompt manipulation, which often yields low attack success

Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models

powered system by invoking external functions. Injection and jailbreaking attacks have been extensively explored to showcase the vulnerabilities of LLMs to user prompt manipulation. The expanded capabilities of agentic models

TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code

enforce safety constraints, just as naive prompting for more secure code, our type-focused agentic pipeline substantially mitigates input validation and injection vulnerabilities. The results demonstrate the potential of structured

InjecMEM: Memory Injection Attack on LLM Agent Memory Systems

personalization and continuity. This naturally prompts a question: will memory system introduce new vulnerabilities into agents? Thus we propose InjecMEM, a novel memory injection attack paradigm that requires only

Reasoning Hijacking: Subverting LLM Classification via Decision-Criteria Injection

which attempts to override the system prompt, Reasoning Hijacking accepts the high-level goal but manipulates the model's decision-making logic by injecting spurious reasoning shortcut. Though extensive experiments

ShadowLogic: Backdoors in Any Whitebox LLM

injecting an uncensoring vector into its computational graph representation. We set a trigger phrase that, when added to the beginning of a prompt into the LLM, applies the uncensoring vector