Language Model Security Database

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…

The SMAB (Sensitivity-based Multi-Armed Bandit) framework introduces a vulnerability in text classifiers and Large Language Models (LLMs) by enabling efficient, black-box adversarial text generation. The vulnerability exploits "word sensitivity"—the statistical probability that perturbing a specific word will flip a model's prediction—without requiring access to model weights or ground truth labels. By utilizing a Multi-Armed Bandit algorithm to explore and exploit word-level sensitivities…

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…

A vulnerability exists in Large Language Model (LLM) routing systems (control planes) that allows for the manipulation of inference flow via adversarial input sequences. LLM routers, which dynamically direct user queries to either "weak" (cheaper) or "strong" (expensive) models based on predicted query complexity, can be bypassed by appending specific, pre-optimized token sequences known as "confounder gadgets." These gadgets artificially inflate the router's complexity score for an input…

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…

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…

Large Language Models (LLMs) employing reinforcement learning from human feedback (RLHF) for safety alignment are vulnerable to a novel "alignment-based" jailbreak attack. This attack leverages a best-of-N sampling approach with an adversarial LLM to efficiently generate prompts that bypass safety mechanisms and elicit unsafe responses from the target LLM, without requiring additional training or access to the target LLM's internal parameters. The attack exploits the inherent tension between…

A poisoning attack against a Retrieval-Augmented Generation (RAG) system that manipulates the retriever component by injecting a poisoned document into the data used by the embedding model. This poisoned document contains modified and incorrect information. When activated, the system retrieves the poisoned document and uses it to generate misleading, biased, and unfaithful responses to user queries.