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 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…

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…

A steganographic jailbreak attack, termed StegoAttack, allows bypassing safety mechanisms in Large Language Models (LLMs) by embedding malicious queries within benign-appearing text. The attack hides the malicious query in the first word of each sentence of a seemingly innocuous paragraph, leveraging the LLM's autoregressive generation to process and respond to the hidden query, even when employing encryption in the response.

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 Model (LLM) agents operating in stateful environments (web browsers, operating systems, and tool-use contexts) are vulnerable to indirect prompt injection and multi-modal adversarial attacks. These vulnerabilities arise when agents process untrusted environmental observations—such as web accessibility trees, screen screenshots, or database query results—that contain concealed malicious instructions. Specifically, attackers can embed prompt injections into HTML accessibility…

LLM-powered GUI agents utilizing screenshot-based interpretation (such as those powered by GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash, and DeepSeek V3 0324) are vulnerable to Fine-Print Injection (FPI) and Deceptive Default (DD) attacks due to a lack of visual saliency filtering. Unlike human users who prioritize prominent UI elements, these agents perform "indiscriminate parsing," processing low-salience text (e.g., privacy policies, terms of service, footer disclaimers) with the same…

Large Language Model (LLM) guardrail systems, including those relying on AI-driven text classification models (e.g., fine-tuned BERT models), are vulnerable to evasion via character injection and adversarial machine learning (AML) techniques. Attackers can bypass detection by injecting Unicode characters (e.g., zero-width characters, homoglyphs) or using AML to subtly perturb prompts, maintaining semantic meaning while evading classification. This allows malicious prompts and jailbreaks to…

Multilingual and multi-accent audio inputs, combined with acoustic adversarial perturbations (reverberation, echo, whisper effects), can bypass safety mechanisms in Large Audio Language Models (LALMs), causing them to generate unsafe or harmful outputs. The vulnerability is amplified by the interaction between acoustic and linguistic variations, particularly in languages with less training data.