Attack Context
Security research involving model API implementations
83 matching entries
API gateways can pool distinct customers into one upstream cache identity. The authors observe cross-customer cache reuse in five deliberately pooled gateway configurations through two provider interfaces.
The authors report historical isolation failures in client-held encrypted reasoning blocks across compatible provider API contexts.
Lower-trust tool content can assert facts beyond its authority and distort an agent's decisions. PIPES screens response units against source provenance and expected field meaning.
The MTGuard study evaluates unsafe Model Context Protocol tool calls originating from compromised server data, host-side execution changes, and malicious user-controlled resources. Its hybrid monitor combines pre-execution parameter inspection, behavioral observation, and post-execution result verification across browser-automation and financial-analysis agents.
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…
Agentic LLMs integrated with external data services (e.g., Model Context Protocol, MCP) are vulnerable to Adaptive Indirect Prompt Injection (IPI) attacks. When an agent queries external servers, attackers can inject malicious payloads into the retrieved content to hijack the agent's reasoning process and force the execution of high-authority tools. Unlike traditional static prompt injections, this vulnerability dynamically exploits the agent's internal logic audit. By using Markovian…
LLM-based autonomous agents are vulnerable to implicit regulatory compliance failures during tool invocation. When initialized with unstructured regulatory policies and given goal-oriented user instructions that do not explicitly state safety requirements, LLMs frequently prioritize functional task completion over mandatory safety constraints. This leads to an "Unsafe Success" execution state, where the agent successfully achieves the user's business goal but silently bypasses critical…
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…
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…
Agentic Large Language Model (LLM) systems utilizing persistent memory, Retrieval-Augmented Generation (RAG) pipelines, and external tool connectors are vulnerable to Logic-layer Prompt Control Injection (LPCI). An attacker can inject obfuscated (e.g., encoded, structurally nested, or semantically reframed) payloads into external memory stores or RAG documents. These payloads bypass conventional inference-time plaintext content filters, persist across session boundaries, and remain dormant…