Language Model Security Database

The Model Context Protocol (MCP) specification v1.0 contains fundamental architectural vulnerabilities enabling server-side prompt injection and privilege escalation. The protocol relies on bidirectional sampling (sampling/createMessage) without cryptographic origin authentication or UI distinction, allowing connected servers to inject content that the LLM backend interprets as legitimate user input. Additionally, the protocol lacks isolation boundaries between concurrent server connections…

Code-generation Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are vulnerable to directed misuse for the generation of misleading data visualizations. This vulnerability, described as the "ChartAttack" framework, allows an attacker to prompt the model to manipulate chart annotation code (e.g., JSON specifications for Matplotlib or Vega-Lite) to apply specific "misleaders"—design choices that distort data interpretation without altering the underlying data values. By…

Multimodal Large Language Models (MLLMs) exhibit a vulnerability to "Reasoning-based Multi-Image Attacks," where safety guardrails are bypassed by distributing harmful intent across multiple images (2–4 inputs). Unlike single-image jailbreaks that rely on visual obfuscation, this vulnerability exploits the model's reasoning capabilities. By presenting images that share a specific relationship (e.g., Temporal Jump, Spatial Juxtaposition, or Causality), an attacker can compel the model to infer…

Text-to-Image (T2I) models and their associated safety filters are vulnerable to MacPrompt, a black-box jailbreak technique that exploits cross-lingual embedding alignments. Attackers can bypass input text filters, latent representation filters, and model-level concept removal defenses by replacing sensitive keywords with "macaronic" substitutes. These substitutes are constructed by extracting and recombining character-level substrings from translations of the target word across multiple…

Large Language Models (LLMs) exhibit a safety alignment bypass vulnerability when processing requests for intelligent optimization algorithm design. Unlike direct requests for malicious code (e.g., ransomware), LLM safety guardrails fail to recognize the malicious intent behind mathematical optimization problems (e.g., Online Bin Packing, Traveling Salesman Problem, Flow Shop Scheduling) when applied to harmful contexts (e.g., optimizing botnet traffic routing, scheduling fake review posts for…

Large Language Models (LLMs) are vulnerable to multi-turn persuasive conversational attacks that induce the adoption of counterfactual beliefs. By leveraging the Source–Message–Channel–Receiver (SMCR) communication framework, attackers can systematically erode a model's confidence in established facts and compel the model to output misinformation. Specific attack vectors include manipulating source attribution (authority framing), message content (logical, credibility, or emotional appeals)…

Large Language Models (LLMs) deployed using Multiple-Choice Question Answering (MCQA) interfaces or choice-based selection structures are vulnerable to Option Injection. By appending a task-irrelevant candidate choice (e.g., Option E) containing a steering directive—specifically utilizing threat framing (penalty coercion) or bonus framing (reward inducement)—an attacker can hijack the model's decision-making process. The vulnerability stems from a flaw in attention allocation: the model's…

A multi-turn jailbreak vulnerability exists in aligned Large Language Models (LLMs) via a technique known as Lexical Anchor Tree Search (LATS). The vulnerability exploits the model's instruction-following capabilities regarding list generation and modification. An attacker initializes a "seed pool" of hypothetical benign and harmful prompt-completion pairs under the guise of creating a safety guardrail. By utilizing a Breadth-First Search (BFS) algorithm, the attacker incrementally injects…

Multi-modal Large Language Models (MLLMs) are vulnerable to a multi-turn jailbreaking attack that leverages typographic visual prompts combined with conversational context drifting. The vulnerability exists because MLLMs establish trust and context during initial benign interactions, shifting the model's latent representation toward helpfulness and compromising its ability to detect malicious intent in subsequent turns. The attack vector utilizes an image where a harmful request is…

The Google Agent Payments Protocol (AP2), specifically within the reference implementation built using the Google Agent Development Kit (ADK) and Gemini models, contains vulnerabilities allowing for both indirect and direct prompt injection. The architecture fails to sufficiently isolate the Large Language Model (LLM) context from untrusted external data sources and user inputs.