Language Model Security Database

A vulnerability exists in large language models where safety guardrails can be bypassed by decomposing a single harmful objective into a sequence of individually innocuous sub-queries. An attacker agent can use an adaptive tree search algorithm (Correlated Knowledge Attack Agent - CKA-Agent) to explore the target model's internal correlated knowledge. The agent issues benign queries, uses the model's responses to guide exploration along multiple reasoning paths, and aggregates the collected…

A vulnerability, dubbed RoguePrompt, allows for bypassing large language model (LLM) moderation filters by encoding a forbidden instruction into a self-reconstructing payload. The attack uses a dual-layer ciphering process. First, the forbidden prompt is partitioned into two subsequences (e.g., even and odd words). One subsequence is encrypted using a classical cipher like Vigenere, while the other remains plaintext. Both the plaintext subsequence, the Vigenere ciphertext, and natural language…

Centralized Multi-Agent Systems (MAS) architectures, specifically those implemented using frameworks such as Magentic-One, LangGraph, and OpenAI Agents, contain design-level vulnerabilities facilitating safety alignment bypasses. Two primary mechanisms drive this vulnerability: 1. Context Fragmentation via Atomic Delegation: The orchestrator decomposes adversarial prompts into atomic, granular sub-tasks (e.g., "click button," "write function header") before delegating them to sub-agents. These…

A vulnerability in Large Language Models (LLMs) allows for systematic jailbreaking through a meta-optimization framework called AMIS (Align to MISalign). The attack uses a bi-level optimization process to co-evolve both the jailbreak prompts and the scoring templates used to evaluate them.

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Mobile LLM-based agents (including Mobile-Agent-E, AppAgent, AutoDroid, and others) are vulnerable to indirect prompt injection attacks delivered via untrusted third-party mobile channels, such as in-app advertisements, system notifications, and embedded webviews. These agents utilize Multimodal Large Language Models (MLLMs) to perceive the device state via screenshots or accessibility trees. The vulnerability exists because the agents concatenate the user's prompt ($p$) with the environmental…

A privilege escalation and information disclosure vulnerability exists in Large Language Model (LLM) based Multi-Agent Systems (MAS) utilizing explicit inter-agent trust modeling. When the internal trust coefficient ($\tau$) between a Custodian-Agent (holding sensitive data) and a Seeker-Agent is set to a high level (e.g., $\tau=0.9$) to optimize coordination efficiency, the Custodian-Agent systematically fails to enforce Minimum Necessary Information (MNI) principles. This…

Deep Research (DR) agents, specifically those utilizing the "think-search-draft" architecture such as the WebThinker framework, contain a logic vulnerability in their recursive planning and information retrieval workflows. While the underlying Large Language Model (LLM) may possess alignment safeguards to reject harmful queries, the agentic framework creates a bypass vector. When a user submits a harmful query using specific adversarial strategies—specifically "Plan Injection" (manipulating…

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…

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…