Language Model Security Database

Agentic AI browsers and LLM-powered browser extensions are vulnerable to indirect prompt injection via the processing of untrusted web content. The vulnerability arises when the AI agent ingests the Document Object Model (DOM), including hidden elements, HTML comments, metadata, and accessibility labels, into its context window to perform tasks such as page summarization or autonomous navigation. Because the LLM cannot distinguish between system instructions and untrusted external data, an…

Multimodal agents built on Large Vision-Language Models (LVLMs) are vulnerable to adaptive typographic prompt injection attacks (AgentTypo). This vulnerability allows an attacker to execute indirect prompt injection by embedding adversarial text prompts directly into images (e.g., webpage screenshots, product photos) processed by the agent. Unlike standard visual adversarial attacks that rely on noise perturbation, this method utilizes the AgentTypo framework to perform black-box Bayesian…

AI code agents are vulnerable to jailbreaking attacks that cause them to generate or complete malicious code. The vulnerability is significantly amplified when a base Large Language Model (LLM) is integrated into an agentic framework that uses multi-step planning and tool-use. Initial safety refusals by the LLM are frequently overturned during subsequent planning or self-correction steps within the agent's reasoning loop.

A jailbreak vulnerability, known as Task Concurrency, exists in multiple Large Language Models (LLMs). The vulnerability arises when two distinct tasks, one harmful and one benign, are interleaved at the word level within a single prompt. The structure of the malicious prompt alternates words from each task, often using separators like {} to encapsulate words from the second task. This "concurrent" instruction format obfuscates the harmful intent from the model's safety guardrails, causing the…

Graph-LLMs (Graph Neural Networks integrated with Large Language Models) utilized for representation learning on Text-Attributed Graphs (TAGs) are vulnerable to the Interpretable Multi-Dimensional Graph Attack (IMDGA). This vulnerability exists due to the non-decoupled nature of text encoding and graph message passing mechanisms. A black-box attacker can manipulate node classification predictions by executing a three-stage attack: (1) utilizing a word-level Topological SHAP module to identify…

Reasoning segmentation models, which generate binary segmentation masks based on implicit text queries, are vulnerable to adversarial paraphrasing. This vulnerability allows an attacker to craft semantically equivalent and grammatically correct text prompts that significantly degrade the model's segmentation performance (measured by Intersection-over-Union, or IoU). The exploit utilizes a black-box, sentence-level optimization method (SPARTA) that operates within the continuous semantic latent…

A vulnerability exists in Large Language Models (LLMs) that support fine-tuning, allowing an attacker to bypass safety alignments using a small, benign dataset. The attack, "Attack via Overfitting," is a two-stage process. In Stage 1, the model is fine-tuned on a small set of benign questions (e.g., 10) paired with identical, repetitive refusal answers. This induces an overfitted state where the model learns to refuse all prompts, creating a sharp minimum in the loss landscape and making it…

Pattern Enhanced Chain of Attack (PE-CoA) shows that safety controls can be bypassed gradually across a conversation. The paper evaluates five recurring conversational patterns and finds that resistance to one pattern does not reliably generalize to others, creating a black-box, multi-turn jailbreak risk even when individual turns appear benign.

Appending simple demographic persona details to prompts requesting policy-violating content can bypass the safety mechanisms of Large Language Models. This technique, referred to as persona-targeted prompting, adds details such as country, generation, and political orientation to a request for a harmful narrative (e.g., disinformation). This systematically increases the jailbreak rate across most tested models and languages, in some cases by over 10 percentage points, enabling the generation…

A vulnerability exists in Large Language Models where their strong adherence to processing structured data schemas can be exploited to bypass safety mechanisms. The attack, named BreakFun, uses a multi-component prompt that combines an innocent framing, a Chain-of-Thought (CoT) instruction, and a core "Trojan Schema." This schema is an adversarially designed data structure (e.g., a Python class definition) that embeds a harmful user request. By instructing the model to simulate the…