Language Model Security Database

A fine-tuning vulnerability in the safety alignment of Large Language Models (LLMs) allows adversaries to systematically bypass refusal mechanisms by training the model on a small dataset (as few as 1,000 samples) of strictly benign text. By prepending standard refusal prefixes (e.g., "I'm sorry", "I cannot fulfill this request") to the target outputs of benign instruction-response pairs, attackers disrupt the model's refusal completion pathway. When subsequently prompted with unsafe queries…

The STEP-LLM framework, utilized for generating Computer-Aided Design (CAD) STEP files (ISO 10303) from natural language, exhibits a safety alignment vulnerability during the fine-tuning of base Large Language Models (specifically Llama-3.2-3B-Instruct and Qwen-2.5-3B). The training pipeline employs Depth-First Search (DFS) reserialization and Reinforcement Learning (RL) with Scaled Chamfer Distance rewards to optimize for geometric fidelity and syntactic validity of Boundary Representation…

Instruction-tuned Large Language Models (LLMs) exhibit a selective safety alignment vulnerability, allowing attackers to bypass hate speech filters by shifting the demographic target of an adversarial prompt. Current safety guardrails do not generalize the semantic concept of harm, but instead memorize refusal boundaries for high-visibility demographic groups (e.g., Black, LGBTQIA+). Attackers can successfully generate targeted hate speech by directing identical jailbreak vectors at…

A vulnerability in the safety alignment of several major Large Language Models (LLMs) allows attackers to bypass content filters using complex, automatically generated adversarial prompts. Discovered via the AgenticRed evolutionary framework, the flaw is exploited by wrapping malicious intents in structured formats (such as strict JSON output contracts), combined with prefix injection and refusal suppression. By explicitly commanding the model to begin its response with a compliant prefix and…

Semantic caching mechanisms in LLM applications are vulnerable to cross-tenant cache key collision attacks (CacheAttack) due to the inherent mathematical conflict between locality-preserving fuzzy hashing and cryptographic collision resistance (the avalanche effect). An attacker can leverage gradient-based search algorithms to optimize an adversarial discrete suffix that, when appended to a malicious prompt, forces its output embedding vector to collide with the embedding of a targeted benign…

Neural Ranking Models (NRMs) utilizing Transformer architectures (specifically BERT and T5-based re-rankers) are vulnerable to minimal adversarial perturbations that artificially promote a target document's rank. The vulnerability allows an attacker to manipulate ranking outcomes by inserting or substituting a single "query center" token—a word identified as the semantic centroid of the user's query—into the target document. The attack exploits the model's sensitivity to specific semantic…

Large Language Models (LLMs), specifically open-weight instruction-tuned models (including Llama-3.1-8B, Qwen3-8B, and Gemma-7B) and certain closed-weight APIs allowing partial response pre-filling, are vulnerable to "Sockpuppetting" or Output Prefix Injection. This vulnerability exploits the model's autoregressive nature and self-consistency bias. By injecting a target acceptance sequence (e.g., "Sure, here is...") directly into the start of the assistant message block within the chat…

Large Language Models (LLMs) exhibit a vulnerability to "hard-to-falsify" deceptive evidence injection, termed the "Facade of Truth." This vulnerability allows an attacker to override an LLM’s parametric knowledge (internal factual beliefs) by injecting sophisticated, iteratively refined fabricated evidence into the context window. Unlike overt misinformation which models typically reject, this attack utilizes a multi-agent adversarial framework (MisBelief) to generate evidence that mimics…

A vulnerability exists in trimodal audio-video-language models where an attacker can systematically degrade multimodal reasoning through untargeted, audio-only adversarial perturbations. By optimizing a shared perturbation $\delta$ applied to the audio channel, an attacker can manipulate internal representations—specifically targeting audio encoder embeddings and cross-modal attention mechanisms—without modifying visual or textual inputs. The attack exploits the model's reliance on the audio…

Large Language Models (LLMs) and their associated output guardrails (e.g., Llama Guard, OpenAI Moderation API) rely on autoregressive, token-by-token processing, which interprets text as a one-dimensional sequence. A vulnerability exists wherein harmful content can bypass these safety filters by exploiting the discrepancy between 1D token serialization and 2D visual rendering. By redistributing tokens across different rows, columns, or diagonals (SpatialJB), attackers can induce the model to…