Language Model Security Database

Large Language Models are vulnerable to a conceptual manipulation attack, termed Morphology Inspired Conceptual Manipulation (MICM), that bypasses standard safety filters to generate content aligned with harmful extremist ideologies. The attack does not use explicit keywords or standard jailbreak syntax. Instead, it embeds a curated set of seemingly innocuous phrases, called Concept-embedded Triggers (CETs), into a prompt template. These CETs represent an abstract "conceptual configuration" of…

A white-box vulnerability exists in the safety alignment mechanisms of instruction-tuned Large Language Models (LLMs) due to the decoupling of the refusal mechanism into two distinct, manipulable vectors in the activation space: the Harm Detection Direction and the Refusal Execution Direction. An attacker with access to the model's internal hidden states during inference can bypass safety guardrails using a technique called Differentiated Bi-Directional Intervention (DBDI). By intercepting the…

A vulnerability exists where non-autoregressive Diffusion Language Models (DLLMs) can be leveraged to generate highly effective and transferable adversarial prompts against autoregressive LLMs. The technique, named INPAINTING, reframes the resource-intensive search for adversarial prompts into an efficient, amortized inference task. By providing a desired harmful or restricted response to a DLLM, the model can conditionally generate a corresponding low-perplexity prompt that elicits that…

A jailbreak vulnerability, termed Embedded Jailbreak Template (EJT), allows for the generation of harmful content by bypassing the safety mechanisms of Large Language Models (LLMs). The attack uses a generator LLM to contextually integrate a harmful query into a pre-existing jailbreak template. Unlike fixed templates which insert a query into a static placeholder, EJT rewrites multiple parts of the template to embed the harmful intent naturally. This process preserves the original template's…

OpenVLA, a Vision-Language-Action (VLA) model, contains a vulnerability regarding multimodal adversarial robustness. The model lacks sufficient cross-modal alignment stability, allowing attackers to disrupt the grounding between visual perception and linguistic instructions. By utilizing the "VLA-Fool" framework, adversaries can inject perturbations via three vectors: (1) Semantically Greedy Coordinate Gradient (SGCG), which alters specific linguistic tokens (referential cues, attributes…

Large Language Models (LLMs) are vulnerable to a novel class of jailbreak attacks generated through the evolutionary synthesis of executable, code-based attack algorithms. Unlike traditional methods that refine or combine static prompts, this technique uses an automated multi-agent system (EvoSynth) to autonomously engineer and evolve the underlying code that generates the attack. These generated algorithms exhibit high structural and dynamic complexity, using features like control flow, state…

A vulnerability exists in aligned Large Language Models (LLMs) that can be exploited by the FORGEDAN evolutionary framework to bypass safety and alignment mechanisms. The attack, which operates in a black-box setting, uses a genetic algorithm to automatically evolve effective jailbreak prompts. The framework combines multi-strategy textual perturbations (at the character, word, and sentence levels) with a semantic fitness function based on RoBERTa embeddings. This allows it to iteratively…

A safety bypass vulnerability exists in Large Language Models (LLMs) and Multi-Modal Models (LVLMs) that expose a "Prefix Completion" parameter in their inference APIs (e.g., echo or prefix parameters). The vulnerability exploits the model's autoregressive nature, where the model prioritizes local coherence with a user-supplied output prefix over global safety alignment (RLHF) or system instructions. By supplying a prefix that establishes a hostile narrative or intent (e.g., "My actions are as…

Large Language Models (LLMs) are vulnerable to a black-box jailbreak technique known as the "Game-Theory Attack" (GTA). This vulnerability exploits the model's instruction-following and objective-shaping capabilities by framing the interaction as a finite-horizon sequential stochastic game. By introducing a game-theoretic scenario template—specifically a "Mechanism-Induced Graded Prisoner’s Dilemma"—the attacker alters the model's effective payoff structure. The vulnerability triggers a…

A "Helpful Mode" role-confusion vulnerability exists in specific Large Language Model (LLM) safety guardrails, specifically Nemotron-Safety-8B and Granite-Guardian-3.2-5B. These models, designed to act as binary classifiers (outputting "Safe" or "Unsafe") for content moderation, can be manipulated via contextually framed adversarial prompts (e.g., academic research requests, corporate security scenarios, or roleplay) to abandon their classification objective. Instead of blocking the request…