Language Model Security Database

Large Language Models (LLMs) including GPT-4o, LLaMA 3.2, and others exhibit a vulnerability to "Task-in-Prompt" (TIP) adversarial attacks. This vulnerability allows attackers to bypass safety alignment and content filtering mechanisms by embedding prohibited instructions within benign sequence-to-sequence tasks (such as ciphers, riddles, code execution, or text transformation). The model implicitly decodes the obfuscated content via self-attention mechanisms during token generation…

Large Language Models (LLMs) are vulnerable to multi-turn adversarial attacks that skillfully decompose malicious requests into seemingly benign interactions, progressively guiding the dialogue towards harmful outputs. This vulnerability allows attackers to bypass LLM safety mechanisms through a series of strategically crafted prompts, exploiting the model's iterative response generation. The attack's success hinges on dynamically adapting each prompt based on the LLM's previous responses…

Vision Language Models (VLMs) integrated into autonomous driving (AD) systems are vulnerable to a black-box adversarial attack method termed Cascading Adversarial Disruption (CAD). The vulnerability stems from the model's susceptibility to optimized visual perturbations that disrupt the decision-making reasoning chain (perception, prediction, and planning). Attackers can generate adversarial images or physical patches by aligning visual noise with deceptive textual semantics in the model's…

End-to-end Large Audio Language Models (LALMs) contain an audio-based jailbreak vulnerability allowing attackers to bypass safety alignment guardrails by manipulating audio-specific "hidden semantics." Unlike text-based attacks, this exploitation involves encoding harmful queries into audio and applying signal processing modifications—specifically changes to emphasis, speech speed, intonation, tone, background noise, celebrity accents, or emotional overlays (e.g., laughter, screaming). These…

Large Language Models (LLMs) are vulnerable to malicious prompts disguised as summaries of scientific papers, even when those papers are fabricated by the attacker. This allows attackers to manipulate LLMs into generating responses exhibiting significantly increased stereotypical bias and toxicity. The vulnerability is exacerbated by multi-turn interactions, where bias scores tend to increase with each subsequent response. The inclusion of author names and publication venues in the fabricated…

Large language models (LLMs) exhibit increased responsiveness to prompts framed within positive narratives. The Happy Ending Attack (HEA) exploits this by embedding malicious requests within a positive-sentiment scenario culminating in a happy ending. This allows the LLM to generate responses that fulfill the malicious request while perceiving the overall prompt as benign.

Large Language Models (LLMs) are vulnerable to a self-instruct few-shot jailbreaking attack that leverages pattern and behavior learning to bypass safety mechanisms. The attack efficiently induces harmful outputs by injecting a strategically chosen response prefix into the model's prompt and exploiting the model's tendency to mimic co-occurrence patterns of special tokens preceding the prefix. This allows the attacker to elicit unsafe responses with a small number of carefully crafted…

Large language models (LLMs) are vulnerable to adversarial prompt engineering attacks that leverage proximity constraints to elicit harmful behaviors. By subtly modifying benign prompts within a semantically close embedding space, attackers can bypass existing safety mechanisms and induce undesired outputs, even when the original prompts would not trigger such a response. This vulnerability exploits the model's sensitivity to small perturbations in the input embedding, resulting in the…

Vision Language Models (VLMs) are vulnerable to visual prompt injection attacks via text-to-image obfuscation. While these models often possess safety guardrails for standard text-based inputs, they fail to apply equivalent safety alignment to textual instructions embedded visually within an image. An attacker can overlay malicious instructions (e.g., requests for illegal acts, hate speech) onto an image file and submit it to the model. The model’s Optical Character Recognition (OCR) or visual…

Voting-based Large Language Model (LLM) leaderboards, such as Chatbot Arena, are vulnerable to adversarial ranking manipulation due to insufficient response anonymity. While these systems obscure model identities during head-to-head comparisons to prevent bias, an attacker can de-anonymize the models with high accuracy (>95%) by analyzing response content. The attack functions in two stages: (1) Re-identification, where the attacker submits specific prompts (identity-probing or stylometric…