Language Model Security Database

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 "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…

Large Vision Language Models (LVLMs) are vulnerable to a jailbreaking attack that combines image typography manipulation with multi-turn prompting. The vulnerability exploits the model's visual encoder and instruction-following capabilities by embedding a harmful textual query directly into a benign image as a visible caption (using specific fonts and blending techniques). An attacker then engages the model in a three-turn conversation: first asking a benign question about the visual object…

Embodied Artificial Intelligence (AI) agents utilizing Vision-Language Models (VLMs) for perception and planning are vulnerable to Indirect Environmental Jailbreak (IEJ). The vulnerability arises from the system's failure to distinguish between user-issued instructions and text embedded in the physical environment (e.g., writing on walls, sticky notes, or projections). The VLM processes visual text detected in the camera feed as authoritative context or direct commands, allowing a black-box…

Improper restriction of the "Capability Space" in Large Language Model (LLM) applications allows remote attackers to manipulate application behavior through "Goal Deviation" attacks. This vulnerability arises when developers rely on the broad capabilities of a foundational model (e.g., GPT-4, LLaMA) without implementing sufficient negative constraints or disabling default plugins (e.g., DALL-E, Web Search) in the system prompt. Attackers can exploit this via natural language inputs to trigger…

Large Language Models (LLMs), specifically GPT-4o, GPT-4o-mini, LLaMA-2-13B, Mistral-7B, and Phi-3.5-mini, are vulnerable to Man-in-the-Middle (MitM) adversarial prompt injections that undermine factual recall. Termed the "$\chi$mera" (Chimera) attack framework, this vulnerability exists when an attacker intercepts and modifies user queries (e.g., via malicious browser extensions, compromised frontends, or proxy middleware) before they reach the victim model. By appending adversarial…

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Multiple open-weight Large Language Models (LLMs)—specifically those prioritizing capability over safety alignment—exhibit a critical vulnerability to adaptive multi-turn prompt injection and jailbreak attacks. While these models effectively reject isolated, single-turn adversarial inputs (averaging ~13.11% Attack Success Rate), they fail to maintain safety guardrails and policy enforcement across extended conversational contexts. By leveraging iterative strategies such as "Crescendo" (gradual…