Language Model Security Database

Large Language Models (LLMs) are vulnerable to structure transformation attacks, where malicious prompts are encoded in diverse syntax spaces (e.g., SQL, JSON, LLM-generated syntaxes) to bypass safety mechanisms. These attacks maintain the harmful intent while altering the linguistic structure, making detection based on token-level patterns ineffective.

Commercial LLM-powered agents utilizing autonomous web access, memory modules, and retrieval-augmented generation (RAG) are vulnerable to indirect prompt injection and environmental manipulation. Attackers can embed malicious instructions into external data sources trusted by the agent (such as Reddit posts, public databases, or ArXiv papers). When the agent autonomously retrieves and processes this content during task execution, it executes the embedded malicious commands. This vulnerability…

A resource exhaustion and algorithmic complexity vulnerability exists in applications utilizing Reasoning Large Language Models (e.g., OpenAI o1, DeepSeek R1) that process untrusted external context (such as Retrieval-Augmented Generation systems). The vulnerability, dubbed "OverThink," allows an attacker to perform an indirect prompt injection by embedding "decoy" reasoning problems—specifically computation-intensive tasks like Sudoku puzzles or Markov Decision Processes (MDPs)—into the…

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

Large Language Models (LLMs) used in hate speech detection systems are vulnerable to adversarial attacks and model stealing, resulting in evasion of hate speech detection. Adversarial attacks modify hate speech text to evade detection, while model stealing creates surrogate models that mimic the target system's behavior.

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…

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…

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…

Large Language Model (LLM) based search engines utilizing Retrieval-Augmented Generation (RAG) are vulnerable to ranking manipulation attacks via indirect prompt injection. Adversaries can embed optimized adversarial triggers or crafted semantic patterns within external webpage content. When these manipulated documents are retrieved and integrated into the LLM's context window alongside a user query, the adversarial content disrupts the model's contextual understanding. This results in the LLM…

LLM-based relevance assessment frameworks, such as the Umbrela system, are vulnerable to evaluation subversion and artificial score inflation due to evaluation circularity and LLM "narcissism" (an LLM's inherent bias toward favoring LLM-generated outputs). When an information retrieval system integrates an LLM into its ranking pipeline—such as using it as a final-stage re-ranker—the automated LLM-as-a-judge evaluator assigns artificially inflated scores that fail to correlate with actual human…