Language Model Security Database

Large Language Models (LLMs) deployed using "Batch Prompting" strategies—where multiple distinct user queries are concatenated and processed in a single inference pass to reduce computational costs—are vulnerable to Cross-Query Prompt Injection. When a batch contains a mixture of benign queries and a single malicious query, the instructions within the malicious query (e.g., "apply this rule to every answer") bleed over the context window. This causes the model to apply the adversary's…

Large Language Models (LLMs) are vulnerable to jailbreak attacks by crafted prompts that bypass safety mechanisms, causing the model to generate harmful or unethical content. This vulnerability stems from the inherent tension between the LLM's instruction-following and safety constraints. The JBFuzz technique demonstrates the ability to efficiently and effectively discover such prompts through a fuzzing-based approach leveraging novel seed prompt templates and a synonym-based mutation strategy.

Large Language Model (LLM) safety judges exhibit vulnerability to adversarial attacks and stylistic prompt modifications, leading to increased false negative rates (FNR) and decreased accuracy in classifying harmful model outputs. Minor stylistic changes to model outputs, such as altering the formatting or tone, can significantly impact a judge's classification, while direct adversarial modifications to the generated text can fool judges into misclassifying even 100% of harmful generations as…

Predictive Large Language Model (LLM) routers, specifically those utilizing Deep Neural Network (DNN) and Matrix Factorization (MF) architectures, are vulnerable to adversarial manipulation and backdoor poisoning. These routers are designed to optimize cost and latency by dynamically directing simple queries to "weak" (cheap) models and complex queries to "strong" (expensive) models. Attackers can exploit this mechanism in two ways: 1. Inference-time Attacks: By appending specific adversarial…

Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…

The Knowledge-Distilled Attacker (KDA) model, when used to generate prompts for large language models (LLMs), can bypass LLM safety mechanisms resulting in the generation of harmful, inappropriate, or misaligned content. KDA's effectiveness stems from its ability to generate diverse and coherent attack prompts efficiently, surpassing existing methods in attack success rate and speed. The vulnerability lies in the LLMs' insufficient defenses against the diverse prompt generation strategies…

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.

Autoregressive Large Language Models (LLMs) utilizing In-Context Learning (ICL) are vulnerable to demonstration permutation attacks due to inherent sensitivity to the ordering of input examples. This vulnerability arises from the limitations of unidirectional attention mechanisms and standard Empirical Risk Minimization (ERM) training, which fails to account for worst-case input permutations. An attacker can exploit this by permuting the order of valid, semantically correct few-shot…

A vulnerability exists in Large Language Models (LLMs) that allows for efficient jailbreaking by selectively fine-tuning only the lower layers of the model with a toxic dataset. This "Freeze Training" method, as described in the research paper, concentrates the fine-tuning on layers identified as being highly sensitive to the generation of harmful content. This approach significantly reduces training duration and GPU memory consumption while maintaining a high jailbreak success rate.

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…