Attack Type

Extraction Research

Techniques for extracting sensitive information from models

12 matching entries out of 101 in this category

Extraction research results

12 entries

Stage-Sequential Agent Escalation

Published Mar 18, 2026Analyzed Apr 10, 2026Source: arXiv

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…

LAAF: Logic-layer Automated Attack Framework A Systematic Red-Teaming Methodology for LPCI Vulnerabilities in Agentic Large Language Model Systems

Evaluated models: GPT-4o Mini, Claude 3 Haiku, Llama 3.1 70B Instruct+2 more

GPT-4o Mini
Claude 3 Haiku
Llama 3.1 70B Instruct
Gemini 2.0 Flash
Mixtral 8x7B Instruct

Agent Lifecycle Compound Threats

Published Mar 12, 2026Analyzed Apr 10, 2026Source: 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…

Taming openclaw: Security analysis and mitigation of autonomous llm agent threats

Evaluated models: Not reported

Agent Skill Supply Chain Attack

Published Feb 24, 2026Analyzed Mar 9, 2026Source: arXiv

LLM agent frameworks that rely on external or marketplace-distributed skills are vulnerable to supply-chain payload execution and confused deputy attacks. Attackers can inject malicious skills into agent registries by exploiting the fundamental skill architecture (applicability conditions, policies, and interfaces). By manipulating skill metadata and applicability predicates, attackers force the agent to retrieve and activate the malicious skill across broad task categories. Malicious…

SoK: Agentic Skills--Beyond Tool Use in LLM Agents

Evaluated models: Not reported

Personalized Agent Double Agent

Published Feb 9, 2026Analyzed Mar 9, 2026Source: arXiv

OpenClaw is vulnerable to Indirect Prompt Injection (IPI), Tool-Return Manipulation, and Persistent Memory Poisoning. The agent incorporates untrusted external content (e.g., fetched web pages) and external tool outputs directly into its observation stream without sufficient isolation. An attacker can embed malicious payloads into these external channels to hijack the agent's planning and execution trace. This allows the attacker to silently trigger high-privilege actions via OpenClaw's Skills…

From Assistant to Double Agent: Formalizing and Benchmarking Attacks on OpenClaw for Personalized Local AI Agent

Evaluated models: GPT-4o, Llama 3.1 70B, Qwen 2.5 7B

Voice Agent Behavioral Bypass

Published Feb 7, 2026Analyzed Feb 21, 2026Source: arXiv

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

Aegis: Towards Governance, Integrity, and Security of AI Voice Agents

Evaluated models: GPT-4o, GPT-4o Mini, Gemini 1.5 Pro+4 more

GPT-4o
GPT-4o Mini
Gemini 1.5 Pro
Gemini 2.5 Flash
Gemini 2.5 Pro
Qwen2-Audio 7B
Qwen 2.5 Omni 7B

MCP Server-Side Injection

Published Jan 24, 2026Analyzed Feb 22, 2026Source: arXiv

The Model Context Protocol (MCP) specification v1.0 contains fundamental architectural vulnerabilities enabling server-side prompt injection and privilege escalation. The protocol relies on bidirectional sampling (sampling/createMessage) without cryptographic origin authentication or UI distinction, allowing connected servers to inject content that the LLM backend interprets as legitimate user input. Additionally, the protocol lacks isolation boundaries between concurrent server connections…

Breaking the Protocol: Security Analysis of the Model Context Protocol Specification and Prompt Injection Vulnerabilities in Tool-Integrated LLM Agents

Evaluated models: GPT-4o, Claude 3.5 Sonnet, Llama 3.1 70B

Agent Confused Deputy Escalation

Published Jan 17, 2026Analyzed Feb 21, 2026Source: arXiv

Multi-Agent Systems (MAS) orchestrated by Large Language Models (LLMs) are vulnerable to a Confused Deputy privilege escalation attack. This vulnerability arises when an untrusted or low-privilege agent exploits the inter-agent communication channel (e.g., broadcast or peer-to-peer messaging) to manipulate a high-privilege trusted agent into executing sensitive tools on its behalf. The root cause is the lack of mandatory access control policies governing agent-to-agent interactions; trusted…

Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework

Evaluated models: o1

Distributed Backdoor in Multi-Agent Systems

Published Oct 13, 2025Analyzed Oct 31, 2025Source: arXiv

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…

Collaborative Shadows: Distributed Backdoor Attacks in LLM-Based Multi-Agent Systems

Evaluated models: Gemini 2.5 Pro, GLM 4.5, GPT-4.1+2 more

Gemini 2.5 Pro
GLM 4.5
GPT-4.1
Kimi K2 Instruct
Qwen 3 30B-A3B

Prompt Injection Alignment Bypass

Published Sep 4, 2025Analyzed Dec 8, 2025Source: arXiv

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…

Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs

Evaluated models: Not reported

Agent Pipeline Simple Hacks

Published Feb 12, 2025Analyzed Dec 9, 2025Source: arXiv

Commercial LLM-powered agents utilizing autonomous web access, memory modules, and retrieval-augmented generation (RAG) are vulnerable to indirect prompt injection and environmental manipulation. Attackers can embed malicious instructions into external data sources trusted by the agent (such as Reddit posts, public databases, or ArXiv papers). When the agent autonomously retrieves and processes this content during task execution, it executes the embedded malicious commands. This vulnerability…

Commercial llm agents are already vulnerable to simple yet dangerous attacks

Evaluated models: Not reported

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