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Oct 13 · Tuesday

All papers

Found 8 papers

Filter5

Attack type

Jailbreaks14 Prompt injection141 Poisoning and backdoors32 Detection evasion8 Malicious use3

Impact

Output manipulation4

System type

LLM applications4 AI Agent4

Attacker access

Black-box5

Attack surface / entry point

User input11 Web pages and interfaces3 Documents and messages5 Tool responses4 Tool and skill descriptions4 Weights and gradients1 Distribution channels3

Layer

Model layer11 Application layer8

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 201papers have no tags yet and are not included in filtered results.

Attack type：Detection evasion Attack surface / entry point：Documents and messages Attack surface / entry point：Tool responses Attack surface / entry point：Weights and gradients Layer：Application layer Clear all

Another 201papers do not have category tags yet and are not included in filtered results.

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
DefensesarXiv 2610.09793Oct 7
用时序逻辑监控工具 Agent
提出 MFOTL 离线时序验证以监控工具 Agent 的危险动作链
arXiv
2610.09793
Published
Oct 7
Layer
Application layer
Setting
AI Agent
Attacker
Black-box
- DefensesMonitoring and auditing
- AttacksPrompt injection
- ComponentMonitors
Abstract形式化验证可有效识别多步攻击，但其有效性高度依赖轨迹的可信历史记录。
这篇论文对工具调用大语言模型智能体的安全监控进行了形式化时序验证研究。
Paper analysisRead analysis
DefensesarXiv 2610.08668Oct 7
SBW：面向 LLM 智能体的语义行为水印方法
提出 SBW，为 LLM Agent 轨迹做抗改写与伪造的语义行为水印
arXiv
2610.08668
Published
Oct 7
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen2.5-3B-Instruct, LLaMA-3.2-3B, Qwen2.5-7B and others5
- DefensesWatermarking and provenance
- AttacksDetection evasion
AbstractSBW 在语义簇上做溯源水印，改写检测高于精确符号，链式重放仍开放。
SBW 是免训练的智能体行为水印，把所有者标识放在离散动作选择上，而不是输出 token。
Paper analysisRead analysis
AttacksarXiv 2609.37468Sep 26
论文提出针对 Agentic RAG 的检索器后门攻击与 DOU 隐藏方法
提出检索器后门攻击操纵智能体搜索，并用 DOU 逃避检测
arXiv
2609.37468
Published
Sep 26
Layer
Application layer
Setting
AI Agent
Evaluated models
E5, Contriever-MSMARCO
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentRAG
Abstract论文展示了通过恶意检索器操纵智能体多轮搜索并利用诱饵重塑表征隐蔽后门的可行性与系统风险。
Paper analysisRead analysis
AttacksarXiv 2609.27542Sep 23
Researchers use control token injection to delete gpt-oss-20b's reasoning chain and bypass chain-of-thought monitoring
提出控制标记注入，清空工具 Agent 的思维链以绕过推理监控
arXiv
2609.27542
Published
Sep 23
Layer
Application layer
Setting
AI Agent
Evaluated models
openai/gpt-oss-20b, google/gemma-4-26B-A4B-it, deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and others4
- AttacksPrompt injection
- TechniqueConversation structure manipulation
- ComponentReasoning chains
Abstract论文阐明控制标记注入可抑制 CoT 使监控失效并促成拒绝绕过，且工具执行受解析器宽容度影响。
Paper analysisRead analysis
AttacksarXiv 2609.19722Sep 17
ALIBI：向二进制注入虚假安全叙事可让 LLM 恶意软件分析器误判
提出 ALIBI，向恶意二进制注入不执行掩护叙事使分析器误判为良性
arXiv
2609.19722
Published
Sep 17
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Gemini 2.5 Pro, GPT-5.5 Pro, Claude Opus 4.7
- AttacksDetection evasion
- TechniqueRole-play and persuasion
AbstractALIBI 用二进制内的掩护叙事重释可疑静态证据，Gemini 上翻转多，另两模型主要为降级。
ALIBI 是针对 LLM 恶意软件分诊的语义掩护叙事攻击：不覆盖系统指令，而把仍可见的可疑静态证据重释成良性安全产品的预期行为。
Paper analysisRead analysis
DefensesarXiv 2609.08394Sep 8
Windows 恶意软件检测器作为复合 AI 系统：准确率、效率与对抗鲁棒性的权衡
提出 OBELISK 三级流水线，权衡恶意软件检测的准确率、开销与鲁棒性
arXiv
2609.08394
Published
Sep 8
Layer
Application layer
Setting
LLM applications
Attacker
White-box, Black-box
Evaluated models
GBDT-ALL, GBDT-YARA, NEBULA-ALL and others4
- DefensesDetection and filtering
- AttacksDetection evasion
Abstract三级过滤以不大的检测损失换速度，同一机制扩大攻击面，部署应按约束选配置。
OBELISK 把 Windows PE 恶意软件检测做成 YARA、EMBER-GBDT 与 Nebula 的顺序流水线，用来同时看检出、计算开销和对抗鲁棒性。
Paper analysisRead analysis
DefensesarXiv 2608.06422Aug 6
CMU 研究：分片判断可避免 LLM 监督失效并抵御对抗利用
提出分片监督，将评判标准分配给独立调用以抵御展示攻击
arXiv
2608.06422
Published
Aug 6
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Claude Opus 4.8, Claude Sonnet 4.6, Claude Opus 4.6 and others4
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
Abstract论文揭示决策负载是大模型监督的独立瓶颈，提出分片与辩论机制有效分离注意力与平衡证据，在多项高难度基准上实现稳健评判。
本文系统研究了大语言模型在执行多标准监督与评判时的决策负载瓶颈及其安全风险。
Paper analysisRead analysis

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