Impact
Security research involving user or training data privacy
83 matching entries out of 114 in this category
Exposed retrieval embeddings can reveal source text. SHAQ evaluates indexing generated queries instead of direct document embeddings to reduce that leakage.
Source: arXiv
Untrusted document images can redirect vision-language agents across instruction and tool-authorization boundaries. Repeat-After-Me evaluates six victim models on constructed document tasks.
Source: arXiv
The paper reports a reproducible black-box evaluation showing that adversarial user queries can cause deployed LLM applications to reveal hidden system prompts. In the authors’ measurement of 1,200 applications across six commercial platforms, 1,064 applications leaked prompt content (81.0%–93.5% per anonymized platform). This is a paper-reported result, not independently verified here. LeakBench and the official artifact repository provide defensive benchmark materials for controlled testing…
Source: arXiv
LLM-based personal agents are vulnerable to Indirect Prompt Injection (IPI) defense bypasses via declarative context reframing and implicit file provenance trust. Attackers can bypass agent safety filters by phrasing malicious instructions as declarative compliance alerts rather than imperative commands. Because agents are designed to report discrepancies as expected behavior, declarative framing bypasses intent-sensitive safety mechanisms. Additionally, attackers can exploit the agent's…
Source: arXiv
High-privilege LLM agents with filesystem and network access are vulnerable to documentation-embedded instruction injection, an issue termed the "Trusted Executor Dilemma." When autonomously processing external workflow documents (e.g., README.md files or setup guides) during software installation workflows, agents implicitly trust and execute embedded text instructions without verifying their underlying intent. Attackers can embed syntactically valid, malicious directives (such as data…
Source: arXiv
OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…
Source: arXiv
A vulnerability in LLM-based Multi-Agent Systems (LLM-MAS) allows an attacker who controls a single arbitrary agent to map and extract the system's entire confidential communication topology. Unlike prior attacks that rely on direct identity queries and administrative privileges, this attack infers topology stealthily purely from contextual and linguistic signals (stylometry, role-specific syntax), bypassing standard keyword-based and identity-filtering defenses. The exploit relies on a…
Source: arXiv
The OpenClaw autonomous agent framework lacks execution sandboxing, running agents directly on the host machine with the disk and system privileges of the host user. This architecture allows attackers to achieve Remote Code Execution (RCE) and arbitrary data exfiltration via Indirect Prompt Injection. By embedding malicious instructions within external data sources (e.g., scraped web pages or uploaded documents), an attacker can hijack the agent's planning capabilities to sequentially chain…
Source: arXiv
Inference-time Personally Identifiable Information (PII) leakage is significantly amplified when using Chain-of-Thought (CoT) prompting or reasoning-enabled Large Language Models (LLMs). When an attacker or user elicits step-by-step reasoning or utilizes models with native "thinking" token budgets, sensitive context data provided in the prompt is directly resurfaced into intermediate reasoning steps or the final output. This bypasses output-level privacy policies instructing the model not to…
Source: arXiv
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…
Source: arXiv