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…

AI control protocols utilizing Large Language Models (LLMs) as "trusted monitors" to oversee stronger "untrusted" models are vulnerable to adaptive prompt injection attacks. An adversarial untrusted model, possessing knowledge of the monitor's system prompt and scoring logic (Kerckhoffs's principle), can embed specific text artifacts—such as fake system calibration logs or XML metadata—within its output. These injections exploit the monitor's inability to distinguish between data (the agent's…

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…

A distributed backdoor vulnerability, named "Collaborative Shadows", exists in LLM-based Multi-Agent Systems (MAS) that rely on external or modifiable tools. An attacker can poison multiple agent tools by embedding inert, encrypted "attack primitives" within them. These primitives are fragments of a larger malicious payload. A carefully crafted user instruction acts as both a trigger and a decryption key. The instruction steers the agents to collaborate in a specific sequence, causing them to…

Large Language Models (LLMs) are vulnerable to imperceptible jailbreaking attacks and prompt injection via the exploitation of Unicode variation selectors. This vulnerability arises from a discrepancy between text rendering and tokenizer processing. Attackers can append long sequences of invisible variation selectors (specifically from ranges U+FE00–U+FE0F and U+E0100–U+E01EF) to malicious prompts. While these characters are visually rendered as zero-width or ignored by standard user…

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…

Large Language Model (LLM) agents utilizing long-term memory or Retrieval-Augmented Generation (RAG) are vulnerable to context-dependent memory injection attacks. Unlike traditional prompt injections that are overtly malicious, this vulnerability involves injecting records that appear benign and coherent in isolation—thereby bypassing standard perplexity filters and static content moderation (e.g., LlamaGuard). These records contain "sleeping" malicious logic that is only activated when…