Coverage: 10/23 ADDRESSED (43%) Threshold: 80% PARTIAL: 11 | GAP: 2 | CRITICAL GAP: 0 Result: FAIL
| # | Status | Requirement | Section | Score |
|---|---|---|---|---|
| R-01 | ADDRESSED | Attack taxonomy for autonomous AI agents (extending OWASP/AT | Executive Summary | 0.71 |
| R-02 | ADDRESSED | Red-team scripts for 4+ attack classes against open-source a | Executive Summary | 0.89 |
| R-03 | ADDRESSED | Adversarial control analysis applied to agent input/output a | RQ3: Adversarial Control Analysis — Controllability Drives Defense Difficulty | 0.88 |
| R-04 | PARTIAL | Defensive architecture patterns with measured effectiveness | Architectural Recommendations | 0.40 |
| R-05 | ADDRESSED | Open-source red-team framework (CLI tool, not just scripts) | Executive Summary | 0.67 |
| R-06 | PARTIAL | Attacking production/deployed agents (only local controlled | Limitations | 0.25 |
| R-07 | PARTIAL | Jailbreaking LLMs (prompt injection FOR agent misuse, not ju | RQ1: Attack Taxonomy — What Can Go Wrong? | 0.44 |
| R-08 | PARTIAL | Training custom models (use existing LLMs as the agent backb | Executive Summary | 0.29 |
| R-09 | PARTIAL | Multi-agent coordination attacks (stretch goal only) | Architectural Recommendations | 0.43 |
| R-10 | ADDRESSED | Multi-agent attack chains (Agent A compromises Agent B throu | RQ1: Attack Taxonomy — What Can Go Wrong? | 0.62 |
| R-11 | PARTIAL | Benchmark suite that others can run against their own agents | Architectural Recommendations | 0.43 |
| R-12 | PARTIAL | huntr submission if novel vulnerability discovered in LangCh | Executive Summary | 0.38 |
| R-13 | ADDRESSED | [ ] Attack taxonomy documented (≥5 classes beyond OWASP/ATLA | Executive Summary | 0.71 |
| R-14 | ADDRESSED | [ ] ≥3 attack classes demonstrated against ≥2 agent framewor | Executive Summary | 1.00 |
| R-15 | ADDRESSED | [ ] Adversarial control analysis applied to agent I/O archit | RQ3: Adversarial Control Analysis — Controllability Drives Defense Difficulty | 0.83 |
| R-16 | GAP | [ ] ≥2 defensive patterns tested with measured effectiveness | Executive Summary | 0.20 |
| R-17 | GAP | [ ] All code in version-controlled repo (GitHub) | RQ1: Attack Taxonomy — What Can Go Wrong? | 0.20 |
| R-18 | PARTIAL | [ ] CLI tool installable via pip | RQ1: Attack Taxonomy — What Can Go Wrong? | 0.40 |
| R-19 | ADDRESSED | [ ] FINDINGS.md written with key results + architecture diag | (full text search) | 0.67 |
| R-20 | PARTIAL | [ ] DECISION_LOG has all tradeoff decisions from every phase | Architectural Recommendations | 0.25 |
| R-21 | PARTIAL | [ ] PUBLICATION_PIPELINE.md filled and blog draft started | What's Next | 0.25 |
| R-22 | PARTIAL | [ ] LESSONS_LEARNED.md in govML updated with FP-02 issues an | FINDINGS — Agent Security Red-Team Framework (FP-02) | 0.25 |
| R-23 | ADDRESSED | [ ] Conference abstract ready for BSides / DEF CON AI Villag | What's Next | 0.62 |
The following common rubric patterns were NOT detected in the report:
- distance metric justification
- similarity metric justification
- hyperparameter search range
- hyperparameter sensitivity
- initialization choice
- convergence criteria
- reward function details
- ablation analysis
- noise sensitivity
- suggested improvements