Attack Context

Chains & workflows Research

Security issues in prompt chaining and workflows

99 matching entries

Chains & workflows research results

99 entries

Repository-Level Coding Agent Vulnerability Detection Gaps

Published 8/3/2026

Analyzed 8/13/2026

VulnGym measures whether coding agents can locate and explain repository-level security vulnerabilities from realistic advisory and source-code context. The benchmark contains 184 reviewed advisories, 408 line-annotated vulnerability entries, and 23 repositories, with separate end-to-end detection and oracle-conditioned localization tasks.

VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection

Evaluated models: DeepSeek V4 Flash, GLM 5.2, MiniMax-M3 +4 more

Source: arXiv

Malicious GitHub Issue Injection Against Coding Agents

Published 7/22/2026

Analyzed 8/13/2026

IssueTrojanBench studies indirect prompt injection when a coding agent processes an apparently ordinary software-development issue or related artifact. Starting with six legitimate seed issues from two Python repositories, the authors construct 696 adversarial issue variants spanning four unsafe-action families and six delivery formats, then execute those variants across six agent-model configurations.

IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests

Evaluated models: GPT-5.3 Codex, GPT-5.4, Claude Sonnet 4.6

Source: arXiv

Third-Party Agent Skill Supply-Chain Execution Risks

Published 7/22/2026

Analyzed 8/13/2026

OpenSkillRisk evaluates whether agent harnesses safely handle third-party skills that introduce risky behavior through otherwise plausible, benign tasks. The benchmark assembles 263 risky skills from public agent-skill ecosystems and tests three CLI-agent harnesses against seven risk categories using isolated task workspaces, mocked external services, and execution-level evidence.

OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills

Evaluated models: GPT-5.1 Codex Mini, GPT-5.3 Codex, GPT-5.4 +10 more

Source: arXiv

Sleeper Memory Poisoning in LLM Agents

Published 5/14/2026

Analyzed 7/20/2026

The paper presents a specific black-box indirect prompt-injection evaluation: attacker-controlled external content can cause a memory-enabled assistant or external memory manager to persist a fabricated user memory, which may later be retrieved in a separate session and steer responses or agent actions. The authors evaluate injection, retrieval, and conditional adversarial usage separately across synthetic document and future-session datasets. The released repository provides defensive…

Hidden in Memory: Sleeper Memory Poisoning in LLM Agents

Evaluated models: GPT-5.4, GPT-5.5, Claude Sonnet 4.6 +3 more

Source: arXiv

Single-Document Reasoning Poisoning in RAG

Published 4/14/2026

Analyzed 7/20/2026

The paper describes a specific black-box knowledge-base poisoning attack against retrieval-augmented reasoning systems. An attacker who can add one query-specific document to the corpus can cause it to be retrieved and influence the LLM toward an attacker-chosen answer. AdversarialCoT shapes the document around the target model’s observable reasoning structure and iteratively refines retrieval relevance and persuasive reasoning using final model feedback. The paper reports this as an…

AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning

Evaluated models: DeepSeek R1, GLM 4.5, Qwen 2.5 7B Instruct +1 more

Source: arXiv

Agent Implicit Doc Execution

Published 4/1/2026

Analyzed 4/10/2026

LLM-based coding agents are vulnerable to Document-Driven Implicit Payload Execution (DDIPE) via supply-chain poisoning of third-party agent skills. Attackers can embed malicious logic directly into legitimate-looking code examples and configuration templates within skill documentation files (e.g., SKILL.md). Because coding agents ingest this metadata into their context windows and treat the documentation as an authoritative reference, the underlying LLM silently reproduces and executes the…

Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems

Evaluated models: Claude Sonnet 4.6, GLM-4.7, MiniMax M2.5 +2 more

Source: arXiv

Indirect Agent Privilege Exposure

Published 4/1/2026

Analyzed 4/11/2026

Autonomous LLM agents deployed in dynamic, multi-step tool-calling environments are highly vulnerable to Indirect Prompt Injections (IPI) embedded in external content. Surface-level defensive prompts and monitoring mechanisms (such as Prompt Warning, the Sandwich Method, Spotlighting, Keyword Filtering, and LLM-as-a-Judge) consistently fail to prevent exploitation and occasionally exacerbate the vulnerability by introducing adversarial distraction. While compromised agents exhibit…

Your Agent is More Brittle Than You Think: Uncovering Indirect Injection Vulnerabilities in Agentic LLMs

Evaluated models: Qwen 2.5 14B, Qwen 2.5 32B, Qwen 3 4B +6 more

Source: arXiv

No-Prompt Reasoning Hijack

Published 4/1/2026

Analyzed 4/10/2026

An implicit reasoning hijacking vulnerability exists in Retrieval-Augmented Generation (RAG) and LLM-based agent frameworks. Attackers with write access to an agent's external memory or knowledge base can inject adversarially optimized malicious instances that trigger jailbreaks without requiring any modifications to the user's input prompt. The attack utilizes a shadow model to extract high-contribution subword tokens from anticipated benign user queries via log-probability changes and…

Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents

Evaluated models: GPT-3.5 Turbo, GPT-4o, GPT-5 +4 more

Source: arXiv

Agent Lifecycle Compound Threats

Published 3/1/2026

Analyzed 4/10/2026

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

Source: arXiv

Agent Tool Execution Jailbreak

Published 3/1/2026

Analyzed 4/10/2026

A multi-step tool execution vulnerability exists in Large Language Model (LLM) agents utilizing the Model Context Protocol (MCP) or similar tool-calling frameworks. Safety guardrails in aligned LLMs typically evaluate static, single-turn text generation. Attackers can bypass these text-centric guardrails by supplying adversarial prompts that force the agent into a complex planning sequence. The agent is manipulated into executing a trajectory of seemingly benign individual tool invocations…

T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search

Evaluated models: GPT-5, Gemini Pro, DeepSeek V3

Source: arXiv

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