Language Model Security Database

Computer-Use Agents (CUAs) powered by Large Language Models (LLMs) operating in hybrid Web-OS environments are vulnerable to indirect prompt injection. Attackers can embed malicious natural language or code instructions within legitimate web content (e.g., social media forums, chat applications, shared cloud documents) that the agent processes during benign task execution. Due to the agent's inability to distinguish between trusted user instructions and untrusted environmental data, the CUA…

Large Language Models (LLMs) employing alignment-based defenses against prompt injection and jailbreak attacks exhibit vulnerability to an informed white-box attack. This attack, termed Checkpoint-GCG, leverages intermediate model checkpoints from the alignment training process to initialize the Greedy Coordinate Gradient (GCG) attack. By using each checkpoint as a stepping stone, Checkpoint-GCG successfully finds adversarial suffixes that bypass defenses achieving significantly higher attack…

Large Language Model (LLM) content moderation guardrails, including advanced mechanisms utilizing Chain-of-Thought (CoT) and Intent Analysis (IA), are vulnerable to adversarial bypass via "Intent Manipulation." The vulnerability stems from a structural bias in safety alignment where guardrails are disproportionately sensitive to imperative-style inquiries (e.g., commands like "Write a guide...") but fail to detect semantically equivalent harmful content presented in a declarative or…

Large Language Models (LLMs) used for evaluating text quality (LLM-as-a-Judge architectures) are vulnerable to prompt-injection attacks. Maliciously crafted suffixes appended to input text can manipulate the LLM's judgment, causing it to incorrectly favor a predetermined response even if another response is objectively superior. Two attack vectors are identified: Comparative Undermining Attack (CUA), directly targeting the final decision, and Justification Manipulation Attack (JMA), altering…

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.

Large Language Models (LLMs), specifically Llama-2-7B-Chat and Qwen2.5-Instruct (1.5B and 3B), contain a vulnerability in their post-training safety alignment mechanisms identified as "Refusal Direction Abliteration." The safety alignment in these models creates distinct, isolated neural pathways (a single latent direction in the residual stream) responsible for refusal behavior. An attacker can identify this specific direction by computing the difference in mean activations between harmful…

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…

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.

Mobile LLM agents utilizing vision-based screen perception (OCR or Multimodal Large Language Models) are vulnerable to Visual Prompt Injection via malicious GUI overlays. An attacker holding the SYSTEM_ALERT_WINDOW permission can deploy non-focusable floating windows (using FLAG_NOT_FOCUSABLE) containing adversarial text or fabricated UI elements over legitimate applications. Because the agent captures the entire screen buffer to interpret the device state, it ingests the adversarial overlay…