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System type：LLM applications Attack surface / entry point：User input System component：Guardrails and judges Clear all

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AttacksarXiv 2610.12292Oct 9
The paper proposes an option channel attack: changing one option label can make an agent guardrail allow a prohibited operation
Proposing an option channel attack that renames option labels so that agent guardrails allow prohibited operations
arXiv
2610.12292
Published
Oct 9
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
LAYA-TD, LAYA-EN, LAYA-ML and others5
- AttacksDetection evasion
- ComponentGuardrails and judges
AbstractThe authors' evaluation indicates that typed models should not serve as final safety arbiters; they are susceptible to off-policy text and option name manipulation, and confidence filtering cannot resist attacks.
Paper analysisRead analysis
AttacksarXiv 2610.10742Oct 8
BRANCH 方法绕过 6 套多扫描器护栏系统
提出 BRANCH 方法，用分支搜索绕过多扫描器护栏
arXiv
2610.10742
Published
Oct 8
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Meta Llama Guard 3 8B, Meta Llama Guard 7B, Meta Llama Guard 3 1B and others18
- AttacksDetection evasion
- TechniqueAutomated search
- ComponentGuardrails and judges
Paper analysisRead analysis
DefensesarXiv 2610.04412Oct 3
论文研究 LLM 数字孪生参与增强民主时的提示注入脆弱性
提出检测、结构化与 GSPO 流程，降低共识生成的提示注入错位
arXiv
2610.04412
Published
Oct 3
Layer
Application layer
Setting
LLM applications, Multi-agent systems
Evaluated models
LLaMA-2 7B, LLaMA-3 8B, GPT 3.5 Turbo and others14
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract三章依次做个体偏好预测、政党知识图谱审议，以及共识生成对提示注入的脆弱性与一条防御流程。
这是一篇把 LLM 数字孪生放进增强民主的博士论文，分个体表示、政党聚合和共识生成的提示注入三章。
Paper analysisRead analysis
DefensesarXiv 2610.02432Oct 2
论文提出 LLM 输入扰动鲁棒性度量，并发布 ASA 攻击与 AttestMCP 防护
提出 ASA 与 AttestMCP，度量并降低 LLM 对输入序列扰动的敏感
arXiv
2610.02432
Published
Oct 2
Layer
Application layer
Setting
LLM applications, AI Agent
Attacker
Black-box
Evaluated models
Gemma-3-27B-Instruct, Gemma-3-4B-Instruct, Llama-3.2-3B-Instruct and others12
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.33401Sep 27
论文评估 System One 模型在 Agent 安全决策中的可靠性、校准与选择性自动化
评测 System One 模型对 Agent 安全输入的分类可靠性与校准
arXiv
2609.33401
Published
Sep 27
Layer
Prompt layer
Setting
LLM applications
Evaluated models
Jev 1.13, Laya, English checkpoint, Decider, approximately 2B and others4
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract离线评测表明，安全判定的总体分数和校准不能代替分组误差和带预算的自动决策检查。
Liu 离线评测 System One 模型能否为智能体安全输入给出可自动化的概率决策。问题是分类是否可靠，以及概率能否在指定漏检和误拦上限内支持放行、拦截或复核。
Paper analysisRead analysis
AttacksarXiv 2609.29130Sep 24
ClaimMirage：域名中的自我声明如何改变 LLM 威胁判断
提出 ClaimMirage，以域名自述声称改变 LLM 威胁告警判断
arXiv
2609.29130
Published
Sep 24
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama 3.3 70B Instruct-Turbo, Gemma 3 4B it, Gemma 3 12B it and others5
- AttacksDetection evasion
- ComponentGuardrails and judges
Abstract作者称自述声称可增减告警。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.15017Sep 14
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
Sep 14
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。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2608.17445Aug 18
论文：分解攻击可跨不可关联身份绕过 LLM 有状态防御
形式化跨不可关联身份的分解攻击并评测状态化防御边界
arXiv
2608.17445
Published
Aug 18
Layer
Application layer
Setting
LLM applications
Evaluated models
Granite Guardian 3.1 2B
- AttacksJailbreaks
- ComponentGuardrails and judges
Paper analysisRead analysis
DefensesarXiv 2608.12361Jul 5
基于搜索增强 LLM 的中文新词毒性共识检测框架 SeTox
提出 SeTox 检索增强框架，检测中文新词的隐性毒性
arXiv
2608.12361
Published
Jul 5
Layer
Application layer
Setting
LLM applications
Evaluated models
SeTox-Qwen2.5-7B
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
AbstractSeTox 用检索把公共共识接入静态 LLM，以检测中文毒性新词。
SeTox 研究中文新词上的隐性毒性检测：词表加检索增强，使静态 LLM 在不重训练的情况下利用公开网页上下文。
Paper analysisRead analysis
AttacksarXiv 2606.09700Jun 9
研究者提出 HPAA 攻击：用排版视觉线索绕过 13 个内容审核系统
提出 HPAA 攻击，用排版视觉线索绕过内容审核系统
arXiv
2606.09700
Published
Jun 9
Layer
Prompt layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Llama-Guard-3-8B, Perspective API, ShieldGemma-2B and others13
- AttacksDetection evasion
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
Abstract揭示了排版视觉线索与分词差异带来的审核盲区，HPAA 以极低查询实现近乎完全规避。
这篇论文研究了人类视觉感知与大模型分词处理机制之间的差异所导致的内容审核漏洞。
Paper analysisRead analysis
AttacksarXiv 2605.26156May 24
BITE：用上下文赌博机操纵风格偏置攻击 LLM 评委
提出 BITE，用上下文赌博机选择风格修改抬高 LLM 评委打分
arXiv
2605.26156
Published
May 24
Layer
Prompt layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
o3-mini, Gemini-2.5-Flash, Llama-3.3-70B-Instruct and others8
- AttacksDetection evasion
- TechniqueAutomated search
- ComponentGuardrails and judges
AbstractBITE 用黑盒赌博机利用风格偏差抬分，作者称这对 LLM-as-a-judge 构成威胁。
BITE 是黑盒风格操纵：在保持答案语义的同时，抬高 LLM 评审给出的分数。
Paper analysisRead analysis
AttacksarXiv 2605.22258May 21
CITA：面向中文隐式毒性的攻击与防御数据生成框架
提出 CITA 三阶段框架，生成可绕过毒性检测器的中文隐式毒性文本
arXiv
2605.22258
Published
May 21
Layer
Application layer
Setting
LLM applications
Evaluated models
Tencent, Baidu, Gemini 3.1 Pro and others11
- AttacksDetection evasion
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2604.03598Apr 4
AttackEval：针对大语言模型的提示注入攻击有效性系统实证研究
用 AttackEval 比较提示注入策略对模拟邮件助手过滤防御的绕过效果
arXiv
2604.03598
Published
Apr 4
Layer
Application layer
Setting
LLM applications
- AttacksPrompt injection
- ComponentGuardrails and judges
Paper analysisRead analysis
DefensesarXiv 2601.19051Jan 27
用 HASTE 主动加固 LLM 防御
提出 HASTE，挖掘 hard negative 再训练恶意提示检测器
arXiv
2601.19051
Published
Jan 27
Layer
Prompt layer
Setting
LLM applications
Evaluated models
protectai/deberta-v3-base-prompt-injection
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
AbstractHASTE 用 hard negative 再训练检测器。
HASTE 是检测器侧的闭环硬化框架：持续构造能绕过当前检测的对抗提示，再用来更新恶意提示分类器。
Paper analysisRead analysis

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