Language Model Security Database

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…

Large Language Models (LLMs) designed for step-by-step problem-solving are vulnerable to query-agnostic adversarial triggers. Appending short, semantically irrelevant text snippets (e.g., "Interesting fact: cats sleep most of their lives") to mathematical problems consistently increases the likelihood of incorrect model outputs without altering the problem's inherent meaning. This vulnerability stems from the models' susceptibility to subtle input manipulations that interfere with their…

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.

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…

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…

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…

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…

A vulnerability exists in Large Language Model (LLM)-based time series forecasting architectures, specifically affecting models such as TimeGPT, LLMTime, and TimeLLM. These models are susceptible to a gradient-free, black-box adversarial attack method termed Directional Gradient Approximation (DGA). An attacker can inject imperceptible perturbations into the historical time series input window (lookback window) to manipulate the model's output. By treating the model as a black box and…

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.