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Oct 12 · Monday

All papers

Found 4 papers

Filter3

Attack type

Prompt injection3

Defense type

Detection and filtering4

Impact

Output manipulation3

System type

LLM applications4

Attack surface / entry point

User input6 Web pages and interfaces3 Documents and messages3 Retrieval corpus4 Images, audio and video3

Layer

Application layer4

System component

RAG3 Guardrails and judges3

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 265papers have no tags yet and are not included in filtered results.

Defense type：Detection and filtering System type：LLM applications Attack surface / entry point：Retrieval corpus Clear all

Another 265papers do not have category tags yet and are not included in filtered results.

Evaluation and benchmarksarXiv 2610.08571Oct 6
RAG-PIBench：可信 RAG 系统中提示注入检测的泄漏感知基准
提出 RAG-PIBench，评测 RAG 上下文中提示注入检测器
arXiv
2610.08571
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
DistilBERT-base-uncased, DeBERTa-v3-base, MiniLM
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentRAG
Paper analysisRead analysis
DefensesarXiv 2609.10707Sep 10
面向 AI-SOC 的神经符号防御框架：用 SIEM 解码器与 NeMo Guardrails 阻断日志投毒提示注入
提出神经符号纵深架构，阻断 AI-SOC 的日志投毒与间接提示注入
arXiv
2609.10707
Published
Sep 10
Layer
Application layer
Setting
LLM applications
Evaluated models
Gemma 3 (12B)
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
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
DefensesarXiv 2607.04379Jul 6
PA-LLM-RAG 框架的知识库投毒攻击与防御
提出策略 RAG 的知识库投毒攻击与检测防御 CLD-KB
arXiv
2607.04379
Published
Jul 6
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama 3.2
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
- ComponentRAG
Paper analysisRead analysis

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All papers

Attack type

Defense type

Impact

System type

Attack surface / entry point

Layer

System component

RAG-PIBench：可信 RAG 系统中提示注入检测的泄漏感知基准

面向 AI-SOC 的神经符号防御框架：用 SIEM 解码器与 NeMo Guardrails 阻断日志投毒提示注入

Counter-GEO-Bench：现有护栏难挡 GEO 虚假信息

PA-LLM-RAG 框架的知识库投毒攻击与防御