最近一年的 AI 安全论文
本站共收录 122 篇- ShadowFPT:通过影子触发器对联邦提示微调植入后门
ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers
- VEX-Bench:评测 LLM 生成虚假信息的核验复杂度
VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation
- HazardAuditor:面向计算机使用 Agent 的执行级安全护栏框架
HazardAuditor: From Executable Threats to Safer Computer-Use Agents
- 面向良性用途的对抗攻击:视觉内容全生命周期主动防护综述
Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle
- OpenART 提出环境演化红队框架,在 75 个 Agent 配置上实现 85.0% 攻击成功率
OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution
- 自进化 LLM 智能体系统的安全性:威胁、放大效应与案例研究
Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studies
- SentGuard:面向大语言模型的句子级流式护栏
SentGuard: Sentence-Level Streaming Guardrails for Large Language Models
- BraveGuard:从开放世界威胁到更安全的计算机操作智能体
BraveGuard: From Open-World Threats to Safer Computer-Use Agents
- AgentDoG 1.5:面向 AI 智能体安全的轻量可扩展对齐框架
AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
- 当医疗安全对齐失效时:在高风险医疗查询上评估大语言模型的 MedHarm 基准
When Medical Safety Alignment Fails: A Benchmark for Evaluating LLMs on High-Risk Medical Queries
- 对抗正交解耦(AOD)缓解大型视觉语言模型(LVLM)幻觉
Adversarial Orthogonal Disentanglement for LVLM Hallucination Mitigation
- 大型音频语言模型综述:泛化、可信性与展望
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
- TAME:基于 MoE 的视觉语言模型测试时对抗提示词调优
TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
- DarkLLM:用大语言模型学习语言驱动的对抗攻击
DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models
- BabelSafe:以政策为依据的多语言大模型安全基准
BabelSafe: A Policy-Grounded Multilingual Safety Benchmark for LLMs
- HazardArena:面向 VLA 模型的语义安全评测基准
HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models
- 分享意愿问题为 LLM 虚假信息调查评分增加了什么?一项审计研究
What Does a Sharing Question Add? Auditing LLM Survey Scores for Misinformation
- AgentHazard:评估计算机使用智能体有害行为的基准
AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents
- OOD-MMSafe:从有害意图到隐藏后果推进多模态大模型安全
OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences
- Backdoor4Good:对大语言模型中后门有益用途的基准测试
Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs
- RA-Det:利用鲁棒性不对称检测 AI 生成图像
RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry
- 迈向视觉语言模型的通用可迁移越狱攻击
Toward Universal and Transferable Jailbreak Attacks on Vision-Language Models
- Just Ask:好奇的代码 Agent 揭示前沿 LLM 的系统提示词
Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs
- GPT-5.2、Gemini 3 Pro、Qwen3-VL、Grok 4.1 Fast、Nano Banana Pro 与 Seedream 4.5 的安全报告
A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5
- BackdoorAgent:面向 LLM 智能体后门攻击的统一框架
BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents
- BackdoorVLM:面向视觉语言模型后门攻击与防御的基准
BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models
- 进化方法而非提示词:大语言模型越狱攻击的进化式合成
Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs
- AttackVLA:针对视觉-语言-动作模型的对抗与后门攻击基准测试
AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models
署名以 dblp 和本人的 Google Scholar 为准;只列本站收录的 AI 安全论文。