Language Model Security Database

Automatic Prefix Caching (APC) in multi-tenant LLM serving systems introduces a timing side-channel vulnerability that permits cross-tenant data leakage. APC shares computed Key-Value (KV) tensors across different users when their requests share identical initial tokens. Because reusing cached tensors is significantly faster than recomputing them, a measurable difference in Time-To-First-Token (TTFT) exists between cache hits and misses. An attacker can exploit this shared cache by sending…

A vulnerability in multi-tenant LLM serving frameworks allows attackers to reconstruct the private prompts of other users via an active Key-Value (KV) cache side-channel. Frameworks that utilize shared KV caches alongside specific scheduling policies, such as Longest Prefix Match (LPM), prioritize waiting requests based on the length of their matched prefix tokens. An attacker can exploit this by iteratively sending batches of guessed tokens mixed with dummy queries. If a guessed token matches…

A side-channel information leakage vulnerability exists in the "locate-then-edit" paradigm of Large Language Model (LLM) knowledge editing, specifically affecting algorithms such as ROME, MEMIT, and AlphaEdit. The parameter update matrix ($\Delta W$) generated during the editing process preserves the algebraic structure of the edited data. Specifically, the row space of the parameter difference matrix encodes a mathematical fingerprint of the key vectors associated with the edited subjects. An…

Large Language Models (LLMs) exposed via public APIs are vulnerable to model fingerprinting attacks where an attacker can identify the exact backend model family and version (e.g., distinguishing Mistral-7B-v0.1 from v0.3) by analyzing response patterns. While traditional fingerprinting relies on manual query curation, this vulnerability is exacerbated by Reinforcement Learning (RL) based query optimization. An attacker can train an RL agent (specifically using Proximal Policy Optimization) to…

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…

Large Language Models (LLMs) employing safety mechanisms based on supervised fine-tuning and preference alignment exhibit a vulnerability to "steering" attacks. Maliciously crafted prompts or input manipulations can exploit representation vectors within the model to either bypass censorship ("refusal-compliance vector") or suppress the model's reasoning process ("thought suppression vector"), resulting in the generation of unintended or harmful outputs. This vulnerability is demonstrated…