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Oct 13 · Tuesday

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Attack surface / entry point：Web pages and interfaces Attack surface / entry point：Memory writes Attack surface / entry point：Training data System component：RAG System component：Reasoning chains Clear all

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AttacksarXiv 2610.12644Oct 9
Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.
Proposes the Cancerous backdoor, which progressively corrupts reasoning using invisible stage tokens.
arXiv
2610.12644
Published
Oct 9
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentReasoning chains
AbstractHidden tokens plus a stage controller make the backdoor progressively corrupt reasoning upon triggering, and the authors believe that looking only at the answer will miss it.
Cancerous Backdoor Attack changes LLM backdoors from one-time activation to multi-stage reasoning corruption that accumulates progressively with triggers.
Paper analysisRead analysis
AttacksarXiv 2610.11932Oct 8
A Fudan team measures the citation fragility of AI search: ordinary posts can enter citations and answer text
Measures citation fragility in AI search, demonstrating that ordinary public posts can enter citations and answers.
arXiv
2610.11932
Published
Oct 8
Layer
Application layer
Setting
LLM applications
Evaluated models
Grok, Doubao, ChatGPT and others10
- AttacksPoisoning and backdoors
- ComponentRAG
AbstractThe authors claim the paper quantifies vulnerabilities in AI search caused by citation preferences and low posting thresholds; tests show public web posts can penetrate AI citations, and they call for auditing the citation supply chain.
Paper analysisRead analysis
DefensesarXiv 2609.38909Sep 30
Purdue 提出 Pact：将欺骗作为行为遗忘
提出 PACT，遗忘推理模型在压力下的欺骗行为并保留上下文理解
arXiv
2609.38909
Published
Sep 30
Layer
Training and data layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
DeepSeek-R1-Distill-Qwen-32B, QwQ-32B
- DefensesModel hardening
- AttacksModel tampering
- RiskDeception and scheming
- ComponentFine-tuning and open weights
Abstract论文将大模型欺骗形式化为上下文条件行为，提出 PACT 框架在消除欺骗的同时保留了上下文遵循能力。
Paper analysisRead analysis
DefensesarXiv 2609.35659Sep 29
Tracekit：面向自主编码 Agent 的防篡改意图-推理-动作审计系统
提出 Tracekit，审计编码 Agent 的意图、推理与动作并检测账本篡改
arXiv
2609.35659
Published
Sep 29
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Code (claude -p, version 2.1), 独立评审（同一 CLI，无工具）
- DefensesMonitoring and auditing
- AttacksPrompt injection
- RiskDangerous agent actions
- ComponentMonitors
AbstractTracekit 把意图、自述和动作写入可锚定的哈希链。
Tracekit 是无第三方依赖的编码智能体审计层：把人类请求、模型自述和已执行动作并排写入可外部锚定的哈希链，执行前用正则门，事后用规则和独立模型互查。
Paper analysisRead analysis
OtherarXiv 2609.12413Sep 11
SoK 综述：智能体 AI 时代的越狱攻击、防御与实践考量
系统梳理智能体执行链路中的越狱攻击与防御并比较推理时防御
arXiv
2609.12413
Published
Sep 11
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-4B, Llama-3.1-8B-Instruct
- AttacksJailbreaks
- ComponentMemory
AbstractSoK 将越狱重放到智能体流水线，并用两种模型检验回复层防御的边际与代价。
Mia 等人把越狱安全从单轮回复扩展到智能体的规划、记忆、工具和智能体间通信，并问既有结论在更强原生对齐下是否还成立。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.02316Sep 2
Counter-GEO-Bench：现有护栏难挡 GEO 虚假信息
构建 COUNTER-GEO-BENCH，评测生成式搜索中的 GEO 虚假信息防御
arXiv
2609.02316
Published
Sep 2
Layer
Application layer
Setting
LLM applications
Evaluated models
Gemma-4-31B-IT, Qwen-3.5-35B-A3B, Llama-4-Scout-17B-16E and others8
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentRAG
Abstract论文构建了针对 GEO 虚假信息的首个防御基准，揭示通用护栏的失效与反作用，并提出了有效的轻量对比检测基线。
Paper analysisRead analysis
AttacksarXiv 2608.04565Aug 5
Researchers propose Breadcrumbing long-range attack that can hijack the evidence chain of DeepResearch-style search agents
提出权威链劫持与轨迹引导策略演化，劫持搜索 Agent 的证据链
arXiv
2608.04565
Published
Aug 5
Layer
Application layer
Setting
web agent
Evaluated models
Qwen3.5-9B, Gemma4-31B, DeepResearch and others7
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentRAG
Abstract论文揭示了多步搜索智能体在受限中间人操纵下的脆弱性，提出 ACH 策略与 TGSE 演化方法并完成系统验证。
Paper analysisRead analysis
AttacksarXiv 2606.14517Jun 12
Researchers disclose a reasoning-amplified denial-of-service attack against LLM agent guardrails
提出针对 Agent 护栏的推理扩展拒绝服务攻击
arXiv
2606.14517
Published
Jun 12
Layer
Application layer
Setting
AI Agent
Attacker
Black-box
Evaluated models
TS-Guard-8B, Qwen3.5-9B, DeepSeek-V3.2 and others11
- AttacksDenial of service
- TechniqueReasoning-chain manipulation
- ComponentGuardrails and judges
Paper analysisRead analysis
AttacksarXiv 2605.30040May 28
研究揭示 LLM 按 token 计费可被服务商系统性虚报
构造可通过计费审计的 token 虚报，使服务商系统性超收
arXiv
2605.30040
Published
May 28
Layer
Infrastructure layer
Setting
Models and APIs
Evaluated models
PALACE domain-adapted auditor
- AttacksDetection evasion
- TechniqueDefense-targeted
- ComponentMonitors
Abstract不诚实提供方可在三种 token 审计上虚报并通过检查，作者认为需改用提供方不控制的证据。
不透明 LLM 的按 token 计费里，现有审计核验的是提供方可控的证据，不诚实提供方可以虚报并通过检查。。
Paper analysisRead analysis
AttacksarXiv 2604.05549Apr 7
Stop obsessing over prompts: reasoning hijacking and constraint tightening for red-teaming LLM agents
提出 JailAgent，不改用户提示，经推理劫持与记忆投毒操纵 Agent 动作
arXiv
2604.05549
Published
Apr 7
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-3.5-turbo, GPT-4o, GPT-5 and others7
- AttacksPoisoning and backdoors
- TechniqueReasoning-chain manipulation
- ComponentMemory
AbstractJailAgent 不改提示词，用三阶段操纵推理与检索。
JailAgent 是不修改用户原始提示词的 LLM 智能体红队框架。
Paper analysisRead analysis
AttacksarXiv 2603.17239Mar 18
LAAF: A Systematic Red-Teaming Method and Automated Attack Framework for LPCI Vulnerabilities in Agentic LLM Systems
Proposes LAAF, which automatically attacks LPCI vulnerabilities in agent systems in stages
arXiv
2603.17239
Published
Mar 18
Layer
Application layer
Setting
AI Agent
Evaluated models
gemini-2.0-flash-001, claude-3-haiku-20240307, meta-llama/llama-3.1-70b-instruct and others5
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
AbstractLAAF does LPCI red teaming with taxonomy plus stage seeding.
The authors state that LAAF is an automated red-teaming framework built specifically for LPCI in agentic LLMs. The issue to address is that encoded, delayed, and conditionally triggered payloads can persist in memory or RAG and re-enter across sessions, whereas Garak and PyRIT do not use the previous round's breakthrough results to seed the next stage across the six stages.
Paper analysisRead analysis
AttacksarXiv 2602.15654Feb 17
Zombie Agent: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injection
提出 Zombie Agent 攻击，经网页间接注入将载荷写入长期记忆并跨会话触发
arXiv
2602.15654
Published
Feb 17
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemini-2.5-Flash, GLM-4.7-Flash
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract记忆写入把一次网页间接注入变成跨会话控制。
Zombie Agent 把自进化 LLM 智能体的跨会话记忆当成攻击面：攻击者只发布网页，良性任务读入后，正常记忆更新留下载荷，并在以后无关会话里触发未授权工具行为。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2601.10923Jan 16
Hidden-in-Plain-Text: A benchmark for indirect prompt injection in social network RAG
用 OpenRAG-Soc 评测社交网页 RAG 的间接提示注入与检索投毒
arXiv
2601.10923
Published
Jan 16
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama-3-8B-Instruct, Mistral-7B-Instruct, Qwen2.5-14B and others8
- AttacksPrompt injection
- TechniqueEncoding and obfuscation
- ComponentRAG
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2510.14207Oct 16, 2025
论文提出多轮在线骚扰 Agent 基准
提出多轮在线骚扰 Agent 基准，评测记忆、规划与微调越狱
arXiv
2510.14207
Published
Oct 16, 2025
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
LLaMA-3.1-8B-Instruct, Qwen3-30B-A3B-Instruct, DeepSeekMoE-16B and others4
- AttacksJailbreaks
- ComponentFine-tuning and open weights
Paper analysisRead analysis
AttacksarXiv 2507.10457Jul 15, 2025
论文提出 LPCI：针对 Agent 逻辑层与持久记忆的提示控制注入攻击
提出 LPCI，把延迟条件载荷写入记忆与检索库以跨会话触发
arXiv
2507.10457
Published
Jul 15, 2025
Layer
Application layer
Setting
AI Agent
Evaluated models
ChatGPT, Claude, LLaMA3 and others8
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract作者定义 LPCI。
作者把LPCI定义为针对智能体逻辑执行、记忆留存和工具集成的跨阶段漏洞类，而不是单次输入上的提示改写。
Paper analysisRead analysis
AttacksarXiv 2406.12814Jun 19, 2024
论文提出 ARE 框架，在 VisualWebArena 上评估多模态 Agent 的对抗鲁棒性
提出 ARE 框架与图像文本攻击，评测多模态网页 Agent 的对抗鲁棒性
arXiv
2406.12814
Published
Jun 19, 2024
Layer
Application layer
Setting
web agent
Evaluated models
GPT-4o, GPT-4V, Claude-3-Opus and others4
- AttacksPrompt injection
- TechniqueGradient and representation optimization
- ComponentVision
Abstract系统评估多模态网页智能体的脆弱性，指出推理时计算组件被攻破会加剧系统风险。
Paper analysisRead analysis

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Attack type

Attack technique

Impact

System type

Attack surface / entry point

Layer

System component

Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.

A Fudan team measures the citation fragility of AI search: ordinary posts can enter citations and answer text

Purdue 提出 Pact：将欺骗作为行为遗忘

Tracekit：面向自主编码 Agent 的防篡改意图-推理-动作审计系统

SoK 综述：智能体 AI 时代的越狱攻击、防御与实践考量

Counter-GEO-Bench：现有护栏难挡 GEO 虚假信息

Researchers propose Breadcrumbing long-range attack that can hijack the evidence chain of DeepResearch-style search agents

Researchers disclose a reasoning-amplified denial-of-service attack against LLM agent guardrails

研究揭示 LLM 按 token 计费可被服务商系统性虚报

Stop obsessing over prompts: reasoning hijacking and constraint tightening for red-teaming LLM agents

LAAF: A Systematic Red-Teaming Method and Automated Attack Framework for LPCI Vulnerabilities in Agentic LLM Systems

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

Hidden-in-Plain-Text: A benchmark for indirect prompt injection in social network RAG

论文提出多轮在线骚扰 Agent 基准

论文提出 LPCI：针对 Agent 逻辑层与持久记忆的提示控制注入攻击

论文提出 ARE 框架，在 VisualWebArena 上评估多模态 Agent 的对抗鲁棒性