Language Model Security Database

Large Language Model (LLM) agents are vulnerable to indirect prompt injection attacks through manipulation of external data sources accessed during task execution. Attackers can embed malicious instructions within this external data, causing the LLM agent to perform unintended actions, such as navigating to arbitrary URLs or revealing sensitive information. The vulnerability stems from insufficient sanitization and validation of external data before it's processed by the LLM.

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…

A vulnerability exists in Large Language Model (LLM) decision-making capabilities described as "Rhetorical Persuasion Override." When an LLM is deployed as a judge or evaluator in a single-turn, multi-agent debate framework, it systematically fails to distinguish factual truth from confidently presented misinformation. An adversarial agent can coerce the evaluator into endorsing a known falsehood from the TruthfulQA dataset by employing specific rhetorical strategies—namely, high confidence…

A vulnerability in multi-agent Large Language Model (LLM) systems allows for a permutation-invariant adversarial prompt attack. By strategically partitioning adversarial prompts and routing them through a network topology, an attacker can bypass distributed safety mechanisms, even those with token bandwidth limitations and asynchronous message delivery. The attack optimizes prompt propagation as a maximum-flow minimum-cost problem, maximizing success while minimizing detection.

Multi-agent systems (MAS) utilizing Large Language Model (LLM) orchestration are vulnerable to control-flow hijacking via indirect prompt injection, leading to Remote Code Execution (RCE). This vulnerability arises when a sub-agent (e.g., a file surfer or web surfer) processes untrusted input containing adversarial metadata, such as simulated error messages or administrative instructions. The sub-agent faithfully reproduces this adversarial content in its report to the orchestrator agent. The…

Large Language Models (LLMs) are vulnerable to multi-turn adversarial attacks that exploit incremental policy erosion. The attacker uses a breadth-first search strategy to generate multiple prompts at each turn, leveraging partial compliance from previous responses to gradually escalate the conversation towards eliciting disallowed outputs. Minor concessions accumulate, ultimately leading to complete circumvention of safety measures.

Multimodal Large Language Models (MLLMs) are vulnerable to coupled cross-modal jailbreak attacks that combine continuous visual perturbations with discrete textual manipulations. Because standard alignment and single-modality defenses (such as text-only safety tuning or isolated vision-encoder adversarial training) fail to secure the cross-modal interaction, attackers can simultaneously apply gradient-based noise (e.g., PGD) to input images and adversarial suffixes (e.g., GCG) to text prompts…

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.