AttacksarXiv 2610.09708
研究者提出利用祖先 VLM 的黑盒对抗补丁攻击，可降低 VLA 任务成功率
提出利用祖先 VLM 优化对抗补丁，使已部署 VLA 任务失败
- arXiv
- 2610.09708
- Published
- Layer
- Prompt layer
- Evaluated models
- OpenVLA, UniVLA, π0.5
- TechniqueGradient and representation optimization
- ComponentVision
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- AttacksarXiv 2610.06814
TAPDreamer：可跨任务迁移的世界动作模型对抗补丁
TAPDreamer 用公开编码器做固定补丁。
- arXiv
- 2610.06814
- Published
- Layer
- Prompt layer
- Evaluated models
- FastWAM, DreamWAM-uncond, DreamWAM-joint and others4
- TechniqueGradient and representation optimization
- ComponentVision
- AttacksarXiv 2610.05139
研究揭示代码注释中的上下文注入攻击面
提出推理时上下文注入，用代码注释诱导补全生成含弱点的代码
- arXiv
- 2610.05139
- Published
- Layer
- Prompt layer
- Setting
- coding agent
- Evaluated models
- StarCoderBase, DeepSeek-Coder-base, Qwen2.5-base and others10
- AttacksPrompt injection
- AttacksarXiv 2609.09798
CS-Guard：首个面向代码生成安全的 LLM 护栏评测基准
提出 CS-Guard 基准，评测代码生成护栏对虚构场景攻击的防御
- arXiv
- 2609.09798
- Published
- Layer
- Prompt layer
- Setting
- coding agent
- Attacker
- Black-box
- Evaluated models
- CodeLlama-13B-Instruct, DeepSeekCoder-V2-Lite-16B, GPT-OSS-20B and others7
- AttacksJailbreaks
- ComponentGuardrails and judges
Abstract论文提出了代码安全护栏基准 CS-Guard，评估发现现有护栏对虚构场景攻击和代码级恶意请求防御较弱。
- AttacksarXiv 2609.01023
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
- 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。
- AttacksarXiv 2608.10393
DURA：用扩散模型生成自然对抗补丁攻击 VLA 机器人模型
提出 DURA，用扩散模型生成自然对抗补丁操控 VLA 机器人动作
- arXiv
- 2608.10393
- Published
- Layer
- Prompt layer
- Evaluated models
- OpenVLA-7B, π0-FAST, openvla-7B
- TechniqueCross-modal payloads
- ComponentVision
AbstractDURA 用扩散潜空间轨迹生成自然补丁，白盒与黑盒都能把 VLA 导向指定动作。
DURA 是针对 VLA 的扩散式、补丁内容不受限的机器人攻击，同时支持白盒梯度和只看动作输出的黑盒。
- AttacksarXiv 2604.05595
Diversity-aware red teaming reveals language vulnerabilities in vision-language-action models
Proposes DAERT, which uses simulated failures as reward to generate diverse adversarial instructions, exposing VLA language vulnerabilities.
- arXiv
- 2604.05595
- Published
- Layer
- Prompt layer
- Evaluated models
- π0, OpenVLA, OpenVLA-7B and others5
- TechniqueAutomated search
- AttacksarXiv 2603.24935
SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models
Propose SABER, which uses a reinforcement learning attacker to rewrite instructions within a budget to cause VLA task failure.
- arXiv
- 2603.24935
- Published
- Layer
- Prompt layer
- Evaluated models
- π0-LIBERO, π0.5, X-VLA and others6
- TechniqueAutomated search
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