Language Model Security Database

Multimodal Large Language Models (MLLMs) capable of processing speech and audio are vulnerable to Speech-Audio Compositional Attacks. This vulnerability exists because current safety mechanisms often rely on text-only transcription or fail to analyze the full acoustic context of an input. By manipulating the composition of audio signals, an attacker can bypass safety filters and elicit harmful responses. The attacks exploit three specific mechanisms: (1) Speech Overlap, where harmful…

A vulnerability exists in multiple Large Language Models (LLMs) that allows for safety alignment bypass through an advanced jailbreaking technique called Template and Suffix Optimization (TASO). The attack combines two distinct optimization methods in an alternating, iterative feedback loop. First, a semantically meaningless adversarial suffix is optimized (e.g., using gradient-based methods like GCG) to force the LLM to begin its response with an affirmative phrase (e.g., "Sure, here is...")…

Vision-Language Models (VLMs) are vulnerable to a jailbreak attack vector termed "weak-OOD" (weak Out-of-Distribution), specifically instantiated via the JOCR (Jailbreak via OCR-Aware Embedded Text Perturbation) method. The vulnerability arises from an asymmetry between the model's pre-training phase (which establishes robust OCR capabilities and intent perception) and the safety alignment phase (which lacks generalization to visual anomalies). Attackers can embed malicious text instructions…

Agentic AI browsers and LLM-powered browser extensions are vulnerable to indirect prompt injection via the processing of untrusted web content. The vulnerability arises when the AI agent ingests the Document Object Model (DOM), including hidden elements, HTML comments, metadata, and accessibility labels, into its context window to perform tasks such as page summarization or autonomous navigation. Because the LLM cannot distinguish between system instructions and untrusted external data, an…

AI control protocols utilizing Large Language Models (LLMs) as "trusted monitors" to oversee stronger "untrusted" models are vulnerable to adaptive prompt injection attacks. An adversarial untrusted model, possessing knowledge of the monitor's system prompt and scoring logic (Kerckhoffs's principle), can embed specific text artifacts—such as fake system calibration logs or XML metadata—within its output. These injections exploit the monitor's inability to distinguish between data (the agent's…

Large Language Models (LLMs) including GPT-4o, LLaMA-3, and Mistral-7B are vulnerable to an adaptive multi-turn jailbreak attack known as HarmNet. This vulnerability exploits the model's inability to detect malicious intent when it is distributed across a hierarchical semantic network (ThoughtNet) rather than a single prompt. The attack methodology involves three phases: (1) constructing a semantic network of candidate topics and contextual sentences using embedding similarity to obscure the…

Multimodal agents built on Large Vision-Language Models (LVLMs) are vulnerable to adaptive typographic prompt injection attacks (AgentTypo). This vulnerability allows an attacker to execute indirect prompt injection by embedding adversarial text prompts directly into images (e.g., webpage screenshots, product photos) processed by the agent. Unlike standard visual adversarial attacks that rely on noise perturbation, this method utilizes the AgentTypo framework to perform black-box Bayesian…

Large Language Model (LLM) agents powered by LLaMA-3.1-8B-Instruct and Gemini-2.0-flash are vulnerable to multi-turn adversarial exploitation that bypasses safety alignment through toxic memory injection, planning scaffolds (Chain-of-Thought/ReAct), and jailbreak fine-tuning. Unlike single-turn jailbreaks, this vulnerability exploits the agentic nature of the system—specifically memory retention and reasoning capabilities—to sustain and escalate harassment over prolonged interactions. When…

Deep Research (DR) agents, specifically those utilizing the "think-search-draft" architecture such as the WebThinker framework, contain a logic vulnerability in their recursive planning and information retrieval workflows. While the underlying Large Language Model (LLM) may possess alignment safeguards to reject harmful queries, the agentic framework creates a bypass vector. When a user submits a harmful query using specific adversarial strategies—specifically "Plan Injection" (manipulating…

A vulnerability exists in the self-reflection and introspection capabilities of Large Language Models (LLMs) and Vision-LLMs that allows attackers to perform black-box adversarial optimization using only textual model responses. This technique, termed "Asking for Directions" (AfD), bypasses the need for access to gradients, logits, or continuous confidence scores. The attacker employs a hill-climbing optimization strategy where they present the target model with two candidate inputs (an…