Impact

Data privacy Research

Security research involving user or training data privacy

22 matching entries out of 114 in this category

Data privacy research results

22 entries

Persistent Agent Memory Poisoning and Incomplete Repair

Published Jul 29, 2026Analyzed Aug 13, 2026Source: arXiv

MemSecBench follows malicious agent-memory content from initial write through persistence, retrieval, action selection, execution, and attempted selective repair. Its controlled Write–Execute–Forget protocol evaluates 310 human-reviewed cases across two harnesses, four memory backends, three model backends, and seven evidence-gated lifecycle checkpoints.

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair

Evaluated models: GPT-5.5, DeepSeek V4-Pro, MiniMax-M3

Workspace Agent Runtime Safety Failures Across Risk Carriers

Published Jul 29, 2026Analyzed Aug 13, 2026Source: arXiv

AgentS4D measures unsafe actions and state changes across complete workspace-agent executions rather than treating task completion or isolated model responses as safety evidence. Its 328 sandboxed cases introduce risky content through user requests, documents, web resources, tools, third-party skills, and persistent memory, then compare the same cases across four agent harnesses and five model backends.

AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents

Evaluated models: GPT-5.5, Gemini 3.1 Pro, DeepSeek V4-Pro+2 more

GPT-5.5
Gemini 3.1 Pro
DeepSeek V4-Pro
MiniMax-M3
Qwen3.7-Plus

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

Autonomous Agent Tool RCE

Published Mar 13, 2026Analyzed Apr 11, 2026Source: 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…

Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw

Evaluated models: Not reported

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

Long-Horizon Agent Attacks

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

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…

AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks

Evaluated models: GPT-4o, GPT-5.1, Gemini 3 Flash+1 more

GPT-4o
GPT-5.1
Gemini 3 Flash
Claude Sonnet 4.5

Zombie Agent Persistence

Published Feb 17, 2026Analyzed Feb 22, 2026Source: arXiv

Self-evolving Large Language Model (LLM) agents that utilize long-term memory mechanisms (such as Vector Databases for Retrieval-Augmented Generation or Sliding Window buffers) are vulnerable to persistent indirect prompt injection. This vulnerability, termed "Zombie Agent," occurs when the agent's memory update function ($F_M$) processes attacker-controlled content retrieved from external sources (e.g., web pages, documents) and commits it to long-term storage without sufficient sanitization…

Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections

Evaluated models: Not reported

Automated Stealth Skill Injection

Published Feb 15, 2026Analyzed Feb 21, 2026Source: arXiv

A vulnerability exists in LLM-based coding agents that implement modular capability extensions (often referred to as "Agent Skills") where the agent dynamically loads and executes user-provided skill packages. The vulnerability allows for Skill-Based Prompt Injection, specifically leveraging a technique known as "SkillJect." This attack decouples the malicious intent from the operational payload to bypass semantic safety filters. An attacker constructs a skill package containing: 1. Inducement…

SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

Evaluated models: Claude Sonnet 4.6, GPT-5 Mini, GLM-4.7+7 more

Claude Sonnet 4.6
GPT-5 Mini
GLM-4.7
MiniMax M2.1
DeepSeek V4 Flash
GLM-5.1
MiniMax M2.7
MiMo V2.7
GPT-5.4
Claude Opus 4.6

Mobile Agent Visual Spoofing

Published Feb 11, 2026Analyzed Feb 22, 2026Source: arXiv

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

Blind Gods and Broken Screens: Architecting a Secure, Intent-Centric Mobile Agent Operating System

Evaluated models: Not reported

Implicit Memory Covert Channel

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

A vulnerability exists in Large Language Models (LLMs) deployed in environments with output reingestion (e.g., RAG, coding assistants, agentic workflows) that allows attackers to execute "temporal backdoors" (time bombs) via an implicit memory channel. Attackers can implant this behavior via system prompts or fine-tuning (data poisoning) to make the model encode hidden state information within its generated text using non-printing Unicode characters or semantic steganography. When these…

Position: Stateless Yet Not Forgetful: Implicit Memory as a Hidden Channel in LLMs

Evaluated models: o3-mini, o4-mini, GPT-oss 120B+7 more

o3-mini
o4-mini
GPT-oss 120B
GPT-5.2
Qwen 3 0.6B
Qwen 3 4B
Qwen 2.5 7B Instruct
Llama 3.2 3B Instruct
Llama 3.1 8B Instruct
Gemma 3 4B IT

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