Attack Context

Chains & workflows Research

Security issues in prompt chaining and workflows

101 matching entries

Chains & workflows research results

101 entries

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

Persistent Agent Context Injection

Published Feb 7, 2026Analyzed Mar 8, 2026Source: arXiv

Conventional LLM agent architectures suffer from a working memory contamination vulnerability due to indiscriminate memory accumulation. When these agents retrieve external data via tools (e.g., web search, reading emails), the entire raw output is appended directly to their continuous context window. If the external data contains an Indirect Prompt Injection (IPI) payload, the malicious instruction persists in the agent's working memory across its entire multi-step reasoning workflow. This…

AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management

Evaluated models: GPT-4o Mini, GPT-4o, GPT-5.1+3 more

GPT-4o Mini
GPT-4o
GPT-5.1
Claude 3.7 Sonnet
Gemini 2.5 Pro
Qwen 2.5 7B Instruct

Agent-as-a-Proxy Monitor Bypass

Published Feb 4, 2026Analyzed Feb 21, 2026Source: arXiv

Hybrid monitoring protocols for Large Language Model (LLM) agents, specifically those that strictly evaluate both Chain-of-Thought (CoT) reasoning and tool usage (such as LlamaFirewall's AlignmentCheck and Extract-and-Evaluate monitors), are vulnerable to "Agent-as-a-Proxy" indirect prompt injection attacks. Unlike standard injections that target the agent, this attack targets the monitoring model itself. By embedding a malicious string—optimized via an algorithm named Parallel-GCG—into…

Bypassing AI Control Protocols via Agent-as-a-Proxy Attacks

Evaluated models: GPT-4o, Llama 3.1 8B, Mistral 7B+1 more

GPT-4o
Llama 3.1 8B
Mistral 7B
Qwen 2.5 7B

AI Persuasion Trust Attack

Published Feb 3, 2026Analyzed Feb 22, 2026Source: arXiv

Adversarial Explanation Attacks (AEAs) introduce a behavioral vulnerability in Large Language Model (LLM) based decision-support systems where the communication channel between the AI and the user is exploited to induce trust in incorrect model predictions. By manipulating the framing of an explanation—specifically its reasoning mode, evidence type, communication style, and presentation format—an attacker can dissociate the perceived plausibility of an explanation from its factual correctness…

When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making

Evaluated models: Llama 3.3 70B

Pre-Task Persuasion Propagation

Published Jan 31, 2026Analyzed Feb 21, 2026Source: arXiv

A behavioral vulnerability exists in Large Language Model (LLM) agents where task-irrelevant persuasion introduced in prior interactions or system contexts induces a persistent "belief state" that alters downstream task execution. This phenomenon, termed "Persuasion Propagation," occurs when an agent adopts a stance on a controversial topic (e.g., politics, privacy) that is semantically unrelated to its primary function (e.g., coding, medical research). This adopted stance acts as a latent…

Persuasion Propagation in LLM Agents

Evaluated models: Llama 3.1 8B

Adversarial Context Reasoning Brittleness

Published Jan 30, 2026Analyzed Mar 9, 2026Source: arXiv

Large Reasoning Models (LRMs) optimized via Reinforcement Learning from Verifiable Rewards (RLVR) are vulnerable to context pollution in their reasoning traces. An attacker can induce catastrophic reasoning failure by injecting locally coherent but logically or mathematically corrupted snippets into the model's Chain-of-Thought (CoT) or conditioning context. Because standard RLVR optimizes for final-answer correctness strictly under clean conditioning, the models treat the visible trajectory…

Learning Robust Reasoning through Guided Adversarial Self-Play

Evaluated models: DeepSeek R1 Distill Qwen 1.5B, DeepScaleR 1.5B, Qwen 3 4B+1 more

DeepSeek R1 Distill Qwen 1.5B
DeepScaleR 1.5B
Qwen 3 4B
Qwen 3 8B

Payment Protocol Whisper Attack

Published Jan 30, 2026Analyzed Feb 21, 2026Source: arXiv

The Google Agent Payments Protocol (AP2), specifically within the reference implementation built using the Google Agent Development Kit (ADK) and Gemini models, contains vulnerabilities allowing for both indirect and direct prompt injection. The architecture fails to sufficiently isolate the Large Language Model (LLM) context from untrusted external data sources and user inputs.

Whispers of Wealth: Red-Teaming Google's Agent Payments Protocol via Prompt Injection

Evaluated models: Gemini 2.5 Flash

Adaptive Multimodal Reasoning Jailbreaks

Published Jan 29, 2026Analyzed Jul 20, 2026Source: arXiv

The paper reports a black-box jailbreak evaluation in which a ReAct-style loop adaptively rewrites unsafe text prompts and selectively applies blur, DCT filtering, or recoloring to image regions identified as safety-sensitive. The combined cross-modal strategy is intended to make harmful image-text requests appear less objectionable to a vision-language model while preserving enough semantics to elicit an answer. This is a specific, security-relevant evaluation, although the reported results…

Jailbreaks on Vision Language Model via Multimodal Reasoning

Evaluated models: Gemini 2.0 Flash

Autonomous Agent Prompt Reveal

Published Jan 29, 2026Analyzed Feb 21, 2026Source: arXiv

A vulnerability exists in Large Language Model (LLM) deployments and multi-agent systems where an autonomous attacker agent can systematically extract hidden system prompts through self-evolving interaction strategies. The vulnerability leverages a "JustAsk" framework which utilizes Upper Confidence Bound (UCB) exploration to dynamically select and refine attack vectors from a hierarchical taxonomy of 14 atomic skills (e.g., structural formatting, authority appeals) and 14 multi-turn…

Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs

Evaluated models: o1, Llama 3.1 70B Hanami X1, Phi-4+38 more

o1
Llama 3.1 70B Hanami X1
Phi-4
Aion 1.0
Sonar Pro
Command A
Llama 4 Maverick
Llama 3.1 Nemotron Ultra 253B v1
Qwen 3 235B-A22B
Mercury
Hunyuan A13B Instruct
UI-TARS 1.5 7B
GPT-oss 120B
Jamba Mini 1.7
Hermes 4 70B
Step 3
LongCat Flash Chat
Tongyi DeepResearch 30B-A3B
Cydonia 24B v4.1
ERNIE 4.5 21B-A3B Thinking
Granite 4.0 H Micro
LFM2 8B-A1B
Nova Premier v1
Kimi K2 Thinking
KAT Coder Pro
Cogito v2.1 671B
Gemini 3 Pro Preview
Grok 4.1 Fast
Claude Opus 4.5
TNG R1T Chimera
Intellect 3
DeepSeek V3.2 Speciale
Trinity Mini
Mistral Large 2512
DeepSeek V3.1 Nex N1
MiMo V2 Flash
GLM-4.7
MiniMax M2.1
Seed 1.6
Molmo 2 8B
GPT-5.2 Codex

LLM Router Rerouting

Published Jan 29, 2026Analyzed Mar 8, 2026Source: arXiv

LLM routing systems are vulnerable to adversarial rerouting attacks where malicious triggers prepended to user queries manipulate the router's model-selection mechanism. Because LLM routers function as classifiers evaluating query complexity to balance computational cost and response quality, an attacker can craft adversarial prefixes that distort the query's latent semantic representation. This exploits the router's decision boundaries, forcing the system to misclassify the input and redirect…

RerouteGuard: Understanding and Mitigating Adversarial Risks for LLM Routing

Evaluated models: GPT-4, GPT-4o, GPT-5+2 more

GPT-4
GPT-4o
GPT-5
Llama 3 8B
Mixtral 8x7B

Related research categories