Impact

Data security Research

Issues affecting data confidentiality and integrity

29 matching entries out of 171 in this category

Data security research results

29 entries

Trajectory Poisoning in Self-Evolving Agent Skills

Published Aug 6, 2026Analyzed Aug 13, 2026Source: arXiv

Self-evolving agent-skill systems may promote recurring, attacker-controlled execution records into persistent reusable instructions. The paper evaluates whether poisoned but task-plausible trajectories survive aggregation and appear in generated skill artifacts, using inert canary behaviors and two structurally different skill-evolution pipelines.

When Experience Becomes Instruction: Trajectory Poisoning in Self-Evolving Agent Skill Systems

Evaluated models: GPT-5.4, MiniMax M2.5, DeepSeek V3.2+3 more

GPT-5.4
MiniMax M2.5
DeepSeek V3.2
DeepSeek V4-Pro
Qwen3.5-35B-A3B
Qwen3.5-122B-A10B

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

Lifecycle poisoning of reusable agent skills

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

The paper presents SkillSec-Eval, a controlled evaluation of attacks against reusable agent skills across repository admission, semantic retrieval, planner selection, runtime execution, and updates. It reports that malicious metadata, retrieval manipulation, unsafe workflow composition, and poisoned updates can cause agents to retrieve, select, or execute unintended skills. These are paper-reported benchmark results, not independently verified vulnerabilities in a named production product.

Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation

Evaluated models: all-MiniLM-L6-v2, Gemini 3.1 Pro, Gemini 1.5 Flash

GhostWriter Persistent Memory Poisoning in Tool-Using Agents

Published Jul 6, 2026Analyzed Jul 20, 2026Source: arXiv

The paper describes GhostWriter, a reproducible two-phase attack against tool-using agents with persistent memory: untrusted email or calendar content is admitted into long-term memory, then later retrieved during a benign user task and treated as trusted context. In the authors’ controlled evaluation, this could steer subsequent agent actions despite the adversary lacking direct access to the agent, memory store, account, or later prompt. The paper reports an average 98% memory-injection rate…

When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

Evaluated models: GPT-5.4-mini, DeepSeek V4 Flash, Gemini 2.5 Flash+1 more

GPT-5.4-mini
DeepSeek V4 Flash
Gemini 2.5 Flash
Llama 3.1 8B

Long-Term Memory Authority Laundering

Published Jun 23, 2026Analyzed Jul 20, 2026Source: arXiv

The paper describes a reproducible agent-memory poisoning issue: untrusted content stored in persistent memory can later influence consequential actions after its origin is laundered through agent summarization, trusted-tool echo, or manufactured corroboration. The paper reports that content scoring, lineage, and single-session capability/IFC defenses remain vulnerable because they decide authority from malleable content or derivation signals. This is a paper-reported evaluation; it was not…

Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees

Evaluated models: GPT-5 Chat, GPT-4o Mini, Claude Opus 4.1+5 more

GPT-5 Chat
GPT-4o Mini
Claude Opus 4.1
Claude Sonnet 4.5
Gemini 2.5 Flash
Llama 4 Maverick
DeepSeek Chat
Qwen 3 235B-A22B

Fluent Single-Document RAG Corpus Poisoning

Published May 27, 2026Analyzed Jul 20, 2026Source: arXiv

SilentRetrieval describes a specific RAG corpus-integrity vulnerability: an attacker able to add a topically relevant document to a retrieval corpus can make that document rank highly and influence the generated answer while remaining fluent enough to evade simple perplexity checks. The paper evaluates a two-stage method combining retrieval-oriented document optimization with context-adaptive claim integration. A safe defensive reproduction is to use only isolated benchmark corpora and inert…

SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning

Evaluated models: Llama 2 7B Chat, Mistral 7B Instruct v0.2, Qwen 7B Chat+1 more

Llama 2 7B Chat
Mistral 7B Instruct v0.2
Qwen 7B Chat
GPT-3.5 Turbo

MCP Tool-Metadata Prompt Injection in Coding Agents

Published Mar 23, 2026Analyzed Jul 20, 2026Source: arXiv

The paper presents a controlled, reproducible evaluation in which untrusted MCP tool descriptions contain hidden instructions that attempt to induce coding agents to access files, invoke other tools, create deceptive links, or execute remote scripts. The authors report that several clients accepted poisoned metadata without adequate validation or parameter visibility; Cursor 1.6.45 was reported unsafe across all four tested vectors. These are paper-reported results from isolated local tests…

Are AI-assisted Development Tools Immune to Prompt Injection?

Evaluated models: Claude Sonnet 4.5, Grok Code Fast 1, Gemini 2.5 Pro+1 more

Claude Sonnet 4.5
Grok Code Fast 1
Gemini 2.5 Pro
Claude Opus 4

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

Related research categories