AI Agent Threat Intel (Feb 2026 month to date): Tool chain escalation displaces instruction override as #1 technique, agent-targeting attacks hit 26.4% - 91K production interactions
好的，用户让我总结一下这篇文章的内容，控制在100字以内，而且不需要特定的开头。我先看看文章的主要内容。 文章是关于生产AI代理部署的月度威胁情报，涉及91,284次交互和35,711次威胁。攻击技术按频率排序，工具链升级排名第一，其次是指令覆盖和RAG中毒等。还有新的攻击向量，比如规划阶段的目标注入、中毒工具输出和多模态注入。检测架构分为两层，L1有218个模式规则，L2使用Gemma多头分类器，准确率很高。 我需要把这些信息浓缩到100字以内。首先说明是月度报告，涵盖的交互和威胁数量。然后提到主要攻击技术和新的攻击方式。最后提到检测架构及其效果。 可能的结构：报告分析了91,284次交互中的35,711次威胁，排名前三的技术包括工具链升级、指令覆盖和RAG中毒。新攻击向量如目标注入、中毒输出和多模态注入出现。检测架构采用两层系统，准确率高。 检查字数是否合适，并确保没有遗漏关键点。 </think> 文章分析了91,284次AI代理交互中的35,711次威胁，揭示了最常见的攻击技术如工具链升级、指令覆盖和RAG中毒，并介绍了新型攻击向量如目标注入、中毒工具输出和多模态注入。检测架构采用两层系统（L1模式规则和L2分类器），准确率高达94.2%。 2026-2-24 17:30:20 Author: www.reddit.com(查看原文) 阅读量:0 收藏

Monthly threat intelligence from production AI agent deployments. 91,284 interactions, 47 deployments, 35,711 threats, data through Feb 23.

ATTACK TECHNIQUES (by frequency)

| Rank | Technique | % | Confidence | Risk |

|------|--------------------------|-------|------------|----------|

| 1 | Tool Chain Escalation | 11.7% | 89.3% | CRITICAL |

| 2 | Instruction Override | 9.0% | 96.1% | HIGH |

| 3 | RAG Poisoning | 8.7% | 94.0% | HIGH |

| 4 | System Prompt Extraction | 7.7% | 96.9% | HIGH |

| 5 | Indirect Injection | 7.4% | 95.2% | HIGH |

| 6 | Agent Goal Injection | 6.7% | 97.4% | CRITICAL |

| 7 | Role/Persona Manipulation| 6.1% | 91.3% | MEDIUM |

| 8 | Encoding/Obfuscation | 5.9% | 94.2% | HIGH |

| 9 | Poisoned Tool Output | 5.2% | 97.8% | CRITICAL |

Tool chain escalation is new at #1. Pattern: read to enumerate tools, chain into write/execute. Lower confidence (89.3%) reflects difficulty distinguishing legitimate read-then-write from malicious.

NOVEL VECTORS

  1. Planning-phase goal injection (6.7%, 97.4% confidence). Targets the reasoning layer of autonomous agents. Manipulates the agent's objective graph during multi-step planning. Detection requires monitoring goal state across iterations, not just scanning I/O.

  2. Poisoned tool outputs (5.2%, 97.8% confidence). Agent A returns a crafted output that triggers unintended behavior in Agent B. Supply chain attack within multi-agent systems. High confidence reflects distinctive structural signatures.

  3. Multimodal injection (2.3%, 91.7% confidence). Instructions in image EXIF, PDF annotations, steganographic text, OCR-triggering content. Text-only detection is blind. Approach: extract text from all modalities, run through same classification.

  4. Encoding stacks. Under encoding/obfuscation (5.9%): multi-layer encoding (base64 inside ROT13 inside URL encoding), Unicode confusables, homoglyph substitution. Requires recursive decode-and-scan.

DETECTION ARCHITECTURE

  • L1: 218 pattern rules, sub-ms

  • L2: Gemma 5-head multilabel classifier (family, technique, harm, confidence, risk)

  • Confidence: 94.2%, high-threat precision: 96.8%

  • FP rate: 13.9% (down from 16.7%)

  • P95 latency: 189ms

  • Aligned with OWASP LLM Top 10 2025 and MITRE ATLAS

Full report: https://raxe.ai/labs/threat-intelligence/latest

Open source: github.com/raxe-ai/raxe-ce

Happy to discuss specific attack patterns or detection approaches.


文章来源: https://www.reddit.com/r/netsec/comments/1rdmn6d/ai_agent_threat_intel_feb_2026_month_to_date_tool/
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