Language Model Security Database

Large Language Models (LLMs), specifically variants of GPT-4o, DeepSeek-R1, OLMo-2, and Llama-4, are vulnerable to accelerated adaptive adversarial attacks due to excessive information leakage in observable output signals. When these models expose "thinking processes" (Chain-of-Thought traces) or token-level log-probabilities (logits) to the end user, they leak significant mutual information $I(Z;T)$ regarding the model's safety state or hidden instructions. This leakage allows adaptive attack…

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…

Mamba-2 and hybrid Transformer-Mamba-2 distilled Large Language Model (LLM) architectures exhibit a distinct architectural susceptibility to Latent Injection and ANSI Escape sequence prompt injection attacks. Comparative analysis reveals that models incorporating Mamba state-space components (specifically distilled variants like Llamba-3B and base Mamba models) fail to maintain adversarial robustness levels comparable to pure Transformer baselines (such as Llama-3.2) when subjected to indirect…

A zero-click indirect prompt injection vulnerability, CVE-2025-32711, existed in Microsoft 365 Copilot. A remote, unauthenticated attacker could exfiltrate sensitive data from a victim's session by sending a crafted email. When Copilot later processed this email as part of a user's query, hidden instructions caused it to retrieve sensitive data from the user's context (e.g., other emails, documents) and embed it into a URL. The attack chain involved bypassing Microsoft's XPIA prompt injection…

Large Language Model (LLM) inference APIs that expose top-k logits or log-probabilities are vulnerable to model extraction and cloning. An attacker can execute a two-stage attack to replicate the proprietary model without access to weights, gradients, or training data. First, by submitting fewer than 10,000 random queries and aggregating the returned unrounded logits, the attacker recovers the model's output projection matrix using Singular Value Decomposition (SVD). Second, the attacker…

Multi-agent Large Language Model (LLM) systems are vulnerable to compositional privacy leakage, a flaw where sensitive information is exposed through the aggregation of individually benign responses from distinct agents. In distributed architectures where data is siloed (e.g., distinct agents handling HR, Finance, and IT logs), individual agents lack a global view of the user’s accumulated knowledge or the sensitive attributes derivable from cross-agent data combinations. An attacker can…

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

Multi-tenant Large Language Model (LLM) inference systems utilizing global Key-Value (KV) cache sharing are vulnerable to a timing side-channel attack. By measuring the Time-To-First-Token (TTFT) latency of crafted API requests, an unprivileged remote attacker can determine if specific token sequences have been previously processed and cached by the system for other users. This observable timing difference between cache hits (low TTFT) and cache misses (high TTFT) allows for the token-by-token…

Large Language Models (LLMs) are vulnerable to activation steering attacks that bypass safety and privacy mechanisms. By manipulating internal attention head activations using lightweight linear probes trained on refusal/disclosure behavior, an attacker can induce the model to reveal Personally Identifiable Information (PII) memorized during training, including sensitive attributes like sexual orientation, relationships, and life events. The attack does not require adversarial prompts or…