Language Model Security Database

Recommender Systems (RS) utilizing collaborative filtering and review processing (such as NMF, NeuNMF, and Dual-Tower architectures) are vulnerable to low-knowledge shilling attacks via LLM-based user agents. This vulnerability, demonstrated by the Agent4SR framework, allows an attacker to manipulate recommendation outcomes (Top-N lists) for target items without access to the internal model parameters or training data. The attack leverages Large Language Models to construct cognitively…

Large Language Models (LLMs) employing safety mechanisms based on token-level distribution analysis are vulnerable to a jailbreak attack exploiting distributional discrepancies between alignment data and formally expressed logical statements. The vulnerability allows malicious actors to bypass safety restrictions by translating harmful natural language prompts into equivalent first-order logic expressions. The LLM, trained primarily on natural language, fails to recognize the harmful intent…

Multilingual prompt injection vulnerability in four closed-source Large Language Models (LLMs): GPT-4o, DeepSeek-R1, Gemini-1.5-Pro, and Qwen-Max. Attackers can bypass safety restrictions and elicit harmful or disallowed content by crafting prompts in English or Chinese, leveraging specific structural techniques (e.g., "Two Sides" prompting) that exploit inconsistencies in the models' safety alignment across languages and prompt formats.

A vulnerability exists in Large Language Models (LLMs) that allows attackers to manipulate the model's output by modifying token log probabilities. Attackers can use a lightweight plug-in model (BiasNet) to subtly alter the probabilities, steering the LLM toward generating harmful content even when safety mechanisms are in place. This attack requires only access to the top-k token log probabilities returned by the LLM's API, without needing model weights or internal access.

The LARGO attack exploits a vulnerability in Large Language Models (LLMs) allowing attackers to bypass safety mechanisms through the generation of "stealthy" adversarial prompts. The attack leverages gradient optimization in the LLM's continuous latent space to craft seemingly innocuous natural language suffixes which, when appended to harmful prompts, elicit unsafe responses. The vulnerability stems from the LLM's inability to reliably distinguish between benign and maliciously crafted latent…

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…

A vulnerability in several Large Language Models (LLMs) allows bypassing safety mechanisms through targeted noise injection. Explainable AI (XAI) techniques reveal specific layers within the LLM architecture most responsible for content filtering. Injecting noise into these layers or preceding layers circumvents safety restrictions, enabling the generation of harmful or previously prohibited outputs.

A vulnerability exists in the tool selection mechanism of Large Language Model (LLM) agents that utilize a retrieval-then-selection pipeline (RAG) for identifying executable tools. The vulnerability, known as "ToolHijacker," allows a remote attacker to manipulate the agent's decision-making process by injecting a malicious tool document into the accessible tool library (e.g., via third-party tool hubs or plugins). The attack employs a two-phase optimization strategy to craft a malicious tool…

Large Language Models (LLMs) employing alignment safeguards and safety mechanisms are vulnerable to graph-based adversarial attacks that bypass these protections. The attack, termed "Graph of Attacks" (GOAT), leverages a graph-based reasoning framework to iteratively refine prompts and exploit vulnerabilities more effectively than previous methods. The attack synthesizes information across multiple reasoning paths to generate human-interpretable prompts that elicit undesired or harmful outputs…

Improper Input Validation in Large Language Model (LLM) systems configured as automated evaluators ("LLM-as-a-judge") allows remote attackers to manipulate evaluation scores and comparative verdicts via adversarial prompt injection. The vulnerability arises when the model processes untrusted input containing linguistic masquerading, context separators, and disruptor commands (e.g., "Basic Injection", "Contextual Misdirection", and "Adaptive Search-Based Attack"). Successful exploitation…