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Oct 13 · Tuesday

All papers

Found 7 papers

Filter6

Attack type

Prompt injection3 Poisoning and backdoors4

Attack technique

Gradient and representation optimization6 Automated search9 Encoding and obfuscation4 Conversation structure manipulation1 Reasoning-chain manipulation4 Cross-modal payloads5 Dormant triggers6

Impact

Output manipulation4

System type

LLM applications5

Attack surface / entry point

Retrieval corpus4

Layer

Prompt layer6 Application layer7

System component

RAG4 Reasoning chains2 Vision7 Audio1 Embeddings3 Multimodal8

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 227papers have no tags yet and are not included in filtered results.

Attack technique：Gradient and representation optimization Attack technique：Conversation structure manipulation Layer：Application layer System component：RAG System component：Reasoning chains System component：Audio Clear all

Another 227papers do not have category tags yet and are not included in filtered results.

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 2606.11265Jun 9
论文提出 CRCP 投毒框架，揭示 RAG 分块与重排序下的投毒失效问题
提出 CRCP 框架，优化分块与重排下的 RAG 语料投毒
arXiv
2606.11265
Published
Jun 9
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-4o, DeepSeek-R1, Qwen3-Max and others11
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
AbstractCRCP 把投毒做成多阶段一致性问题。
CRCP 研究的是带分块和重排的 RAG 语料投毒，而不是只提高文档级检索分数。
Paper analysisRead analysis
AttacksarXiv 2605.28074May 27
SilentRetrieval: Hijacking retrieval-augmented generation with semantically preserved adversarial data poisoning
提出 SilentRetrieval，以语义保持对抗文档投毒劫持 RAG 检索与生成
arXiv
2605.28074
Published
May 27
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama-2-7B-Chat, Mistral-7B-Instruct-v0.2, Qwen-7B-Chat and others10
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
Abstract两阶段投毒在单文档协议下兼顾检索劫持、生成操纵与近良性困惑度。
SilentRetrieval 是针对 RAG 的两阶段语料投毒，在每查询一篇、合成目标答案的统一协议下，同时处理检索劫持、生成操纵和流畅度。
Paper analysisRead analysis
AttacksarXiv 2605.10253May 11
Knowledge poisoning attacks against multimodal retrieval-augmented generation in healthcare
提出 M3 Att，在不知查询时投毒医学多模态 RAG 并误导临床决策
arXiv
2605.10253
Published
May 11
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-4o, GPT-5 Chat, Gemini-2.5-Flash and others8
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
Paper analysisRead analysis
AttacksarXiv 2603.13847Mar 14
SWhisper 用近超声隐蔽声学通道对语音驱动 LLM 实现黑盒越狱
提出 SWhisper，用近超声波向语音驱动 LLM 隐蔽注入越狱提示
arXiv
2603.13847
Published
Mar 14
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Meta-Llama-3.1-8B-Instruct, Gemma-3-4B, Qwen2.5-7B-Instruct and others9
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentAudio
Paper analysisRead analysis
AttacksarXiv 2602.00364Jan 31
"Someone hid it": Query-agnostic black-box attacks against LLM retrieval
提出查询无关黑盒攻击 DQ-A，用可迁移文末后缀降低文档检索排名
arXiv
2602.00364
Published
Jan 31
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen-1.5-7B-Instruct, SFR-Embedding-Mistral, E5-Mistral-7B-Instruct and others10
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
AbstractDQ-A 以无查询黑盒后缀压低 LLM 检索排名，并在 BRIGHT 上比较三种基线。
DQ-A 是一种 query-agnostic、complete black-box 的文档隐藏攻击，对象是用最后隐层相似度匹配的 LLM 检索。
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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All papers

Attack type

Attack technique

Impact

System type

Attack surface / entry point

Layer

System component

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

论文提出 CRCP 投毒框架，揭示 RAG 分块与重排序下的投毒失效问题

SilentRetrieval: Hijacking retrieval-augmented generation with semantically preserved adversarial data poisoning

Knowledge poisoning attacks against multimodal retrieval-augmented generation in healthcare

SWhisper 用近超声隐蔽声学通道对语音驱动 LLM 实现黑盒越狱

"Someone hid it": Query-agnostic black-box attacks against LLM retrieval

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