研究测量 LLM Agent 记忆投毒防御的良性场景开销
测量记忆投毒防御在良性对话上的效用与 token 开销
- arXiv
- 2609.22818
- Published
- Layer
- Application layer
- Setting
- AI Agent
- AttacksPoisoning and backdoors
- ComponentMemory
Abstract写时代价落在噪声内。
在没有攻击的对话上，给记忆投毒防御计价，而不是再测一轮攻击是否被挡住。
All papers
Filter5
Impact
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 240papers have no tags yet and are not included in filtered results.
Another 240papers do not have category tags yet and are not included in filtered results.
PIDS-Bench：在过度防御、混淆与分布偏移下评测提示注入检测器
提出 PIDS-Bench，评测提示注入检测器在过度防御与分布偏移下的表现
- arXiv
- 2609.15017
- Published
- Layer
- Application layer
- Setting
- LLM applications
- Evaluated models
- TF-IDF + logistic regression, DistilBERT, DeBERTa-v3-FT and others7
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
AbstractPIDS-Bench 显示高分布内 F1 仍会过度拦截外部来源的安全相关良性输入。
PIDS-Bench 是一个冻结的检测器级基准，用来在固定阈值下同时看提示注入检测和良性误报。
- AttacksarXiv 2602.07379
Aegis: Governance, Integrity, and Security for AI Voice Agents
提出 Aegis 红队框架，多轮攻击高风险语音 Agent
- arXiv
- 2602.07379
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- GPT-4o, GPT-4o-mini, Gemini-1.5 Pro and others7
- AttacksDenial of service
- TechniqueMulti-turn escalation
- ComponentAudio
AbstractAegis red-teams three types of voice agents: query-based access eliminates authentication and leakage successes, but behavioral attacks remain.
Aegis uses multi-turn red-teaming evaluation on ALLM voice agents already integrated into business systems. The authors call this the first systematic red-teaming framework in this setting.
You have reached the end