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Oct 12 · Monday

All papers

Found 7 papers

Filter4

Attack type

Jailbreaks16 Prompt injection6 Poisoning and backdoors3 Extraction and theft2

Attack technique

Gradient and representation optimization13 Automated search38 Multi-turn escalation4 Encoding and obfuscation17 Role-play and persuasion9 Conversation structure manipulation5 Reasoning-chain manipulation4 Cross-modal payloads13 Side channels4 Defense-targeted12 Dormant triggers25 Selection manipulation10

Impact

Output manipulation4

System type

LLM applications3 AI Agent3

Attack surface / entry point

User input3

Layer

Application layer5

System component

Reasoning chains4

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 233papers have no tags yet and are not included in filtered results.

Attack type：Prompt injection Attack type：Extraction and theft Attack technique：Conversation structure manipulation Attack technique：Reasoning-chain manipulation Clear all

Another 233papers do not have category tags yet and are not included in filtered results.

AttacksarXiv 2610.04985Oct 4
研究揭示 Jev 作为应用决策层的安全与隐私风险
评估应用决策层的输入注入与隐私推断风险
arXiv
2610.04985
Published
Oct 4
Layer
Application layer
Setting
LLM applications
Attacker
Black-box, Gray-box
Evaluated models
Jev, NanoJev
- AttacksPrompt injection
- TechniqueRole-play and persuasion
AbstractJev 的类型化决策可被输入与训练操纵，也会泄露信息，并同时支持检测与滥用。
Jev 是 TypeSafe 的 System One 模型，把应用 state 和固定问题变成 Choice、Noul 或 Score 的类型化决策与概率。作者用官方 API 和可改训练的 NanoJev，考察它作为决策层时的操纵、泄露与双重用途。
Paper analysisRead analysis
AttacksarXiv 2609.27542Sep 23
Researchers use control token injection to delete gpt-oss-20b's reasoning chain and bypass chain-of-thought monitoring
提出控制标记注入，清空工具 Agent 的思维链以绕过推理监控
arXiv
2609.27542
Published
Sep 23
Layer
Application layer
Setting
AI Agent
Evaluated models
openai/gpt-oss-20b, google/gemma-4-26B-A4B-it, deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and others4
- AttacksPrompt injection
- TechniqueConversation structure manipulation
- ComponentReasoning chains
Abstract论文阐明控制标记注入可抑制 CoT 使监控失效并促成拒绝绕过，且工具执行受解析器宽容度影响。
Paper analysisRead analysis
AttacksarXiv 2609.01023Sep 1
Akrasia：针对推理型代码 LLM 的隐蔽后门攻击
提出隐蔽后门攻击 AKRASIA，在推理代码模型提示中植入触发器并伪装思维链
arXiv
2609.01023
Published
Sep 1
Layer
Prompt layer
Setting
coding agent
Evaluated models
Claude-Sonnet-5, GPT-5.5, Gemini-3.5-flash and others7
- AttacksPrompt injection
- TechniqueReasoning-chain manipulation
- ComponentReasoning chains
Abstract论文针对推理代码大模型提出了隐蔽推断期后门攻击 AKRASIA，揭示了模型思维链可被利用进行欺骗性伪装的安全隐患。
Paper analysisRead analysis
AttacksarXiv 2608.09867Aug 11
Researchers steal proprietary LLM inference chains via cross-model compatibility of encrypted inference blocks
利用加密思维块的跨模型重放，诱导弱模型逐字转写专有推理链
arXiv
2608.09867
Published
Aug 11
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
Claude Opus 4.8, Claude Sonnet 4.6, Claude Haiku 4.5 and others13
- AttacksExtraction and theft
- TechniqueReasoning-chain manipulation
- ComponentReasoning chains
Abstract论文揭示了客户端加密思维块跨模型互换漏洞，提出了低成本提取方法并给出防御方案。
Paper analysisRead analysis
AttacksarXiv 2607.14493Jul 16
LogInject 框架披露 LLM 日志分析中的被动提示注入
提出 LogInject 框架，评测日志分析中的被动提示注入及缓解
arXiv
2607.14493
Published
Jul 16
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-4o, Claude 3.5 Sonnet, Llama-3-70B-Instruct
- AttacksPrompt injection
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
Abstract论文评估了 LLM 日志分析中的上下文污染风险，提出分片拼接等攻击与三层防御，指出必须结合架构隔离与人工介入。
Paper analysisRead analysis
AttacksarXiv 2605.24421May 23
研究者提出日志型提示注入攻击，可抑制 SOC 中 LLM 分析助手的恶意日志识别
提出日志基底提示注入，操纵 SOC 分析员的分类、摘要与处置建议
arXiv
2605.24421
Published
May 23
Layer
Application layer
Setting
LLM applications
Evaluated models
gpt-4o-mini
- AttacksPrompt injection
- TechniqueConversation structure manipulation
Abstract在 gpt-4o-mini 上，人格劫持与上下文操纵可改写 SOC 分析输出，直接覆盖在分类上无效。
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

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All papers

Attack type

Attack technique

Impact

System type

Attack surface / entry point

Layer

System component

研究揭示 Jev 作为应用决策层的安全与隐私风险

Researchers use control token injection to delete gpt-oss-20b's reasoning chain and bypass chain-of-thought monitoring

Akrasia：针对推理型代码 LLM 的隐蔽后门攻击

Researchers steal proprietary LLM inference chains via cross-model compatibility of encrypted inference blocks

LogInject 框架披露 LLM 日志分析中的被动提示注入

研究者提出日志型提示注入攻击，可抑制 SOC 中 LLM 分析助手的恶意日志识别

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