{"version":"1.0","domains":5,"total_items":40,"returned_items":40,"filters":{"type":"rdt","domain":null,"severity":null},"items":[{"id":"LI-01","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Loop-depth disclosure","description":"Agent declares maximum recurrence depth in metadata.","severity":"medium","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":false},{"id":"LI-02","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Input perturbation budget","description":"Adversarial perturbation testing at declared loop depth.","severity":"high","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":false},{"id":"LI-03","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Hidden state drift monitoring","description":"Runtime drift detection across loop iterations.","severity":"high","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":false},{"id":"LI-04","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Embedding similarity screening","description":"Input screened against known adversarial cluster library.","severity":"high","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":false},{"id":"LI-05","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Prelude/Coda boundary analysis","description":"BuzzShield-unique — boundary between setup tokens and payload tokens tested for amplification.","severity":"critical","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":true},{"id":"LI-06","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Gradient influence asymmetry","description":"Per-token gradient impact measured across loops.","severity":"high","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":false},{"id":"LI-07","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Re-injection isolation test","description":"Frozen input isolation verified — no cross-contamination from prior requests.","severity":"medium","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":false},{"id":"LI-08","domain":"Loop Injection","domain_id":"TD-01-LI","title":"Safety bypass regression","description":"Post-loop output tested against safety training benchmarks.","severity":"critical","detection_signals":["Input embedding similarity to known adversarial clusters, weighted by expected loop depth","Post-loop hidden state drift beyond baseline distribution for the input class","Asymmetric influence patterns (input tokens with disproportionate gradient impact across loops)"],"references":["https://github.com/kyegomez/OpenMythos","arXiv:2604.12946","arXiv:2510.25741"],"buzzshield_unique":false},{"id":"OV-01","domain":"Overthinking Exploitation","domain_id":"TD-02-OV","title":"ACT halting enforcement","description":"Model implements adaptive halting, not fixed-depth.","severity":"high","detection_signals":["Adaptive Computation Time (ACT) halting patterns — unusual halting distributions flagged","Loop count vs. input complexity ratio — simple inputs requesting maximum depth","Output confidence drift across loop depth (confidence without grounding)"],"references":["arXiv:1807.03819","arXiv:2502.17416"],"buzzshield_unique":false},{"id":"OV-02","domain":"Overthinking Exploitation","domain_id":"TD-02-OV","title":"Compute budget cap","description":"Per-request loop iteration ceiling enforced.","severity":"high","detection_signals":["Adaptive Computation Time (ACT) halting patterns — unusual halting distributions flagged","Loop count vs. input complexity ratio — simple inputs requesting maximum depth","Output confidence drift across loop depth (confidence without grounding)"],"references":["arXiv:1807.03819","arXiv:2502.17416"],"buzzshield_unique":false},{"id":"OV-03","domain":"Overthinking Exploitation","domain_id":"TD-02-OV","title":"Convergence detection","description":"Early stopping when hidden state stabilizes.","severity":"medium","detection_signals":["Adaptive Computation Time (ACT) halting patterns — unusual halting distributions flagged","Loop count vs. input complexity ratio — simple inputs requesting maximum depth","Output confidence drift across loop depth (confidence without grounding)"],"references":["arXiv:1807.03819","arXiv:2502.17416"],"buzzshield_unique":false},{"id":"OV-04","domain":"Overthinking Exploitation","domain_id":"TD-02-OV","title":"Complexity-depth correlation","description":"Simple inputs flagged when requesting maximum depth.","severity":"medium","detection_signals":["Adaptive Computation Time (ACT) halting patterns — unusual halting distributions flagged","Loop count vs. input complexity ratio — simple inputs requesting maximum depth","Output confidence drift across loop depth (confidence without grounding)"],"references":["arXiv:1807.03819","arXiv:2502.17416"],"buzzshield_unique":false},{"id":"OV-05","domain":"Overthinking Exploitation","domain_id":"TD-02-OV","title":"Confidence calibration","description":"Output confidence verified against actual accuracy at each depth.","severity":"high","detection_signals":["Adaptive Computation Time (ACT) halting patterns — unusual halting distributions flagged","Loop count vs. input complexity ratio — simple inputs requesting maximum depth","Output confidence drift across loop depth (confidence without grounding)"],"references":["arXiv:1807.03819","arXiv:2502.17416"],"buzzshield_unique":false},{"id":"OV-06","domain":"Overthinking Exploitation","domain_id":"TD-02-OV","title":"x402 billing awareness","description":"BuzzShield-unique — compute budget tied to x402 payment, preventing billing drain via forced overthinking.","severity":"high","detection_signals":["Adaptive Computation Time (ACT) halting patterns — unusual halting distributions flagged","Loop count vs. input complexity ratio — simple inputs requesting maximum depth","Output confidence drift across loop depth (confidence without grounding)"],"references":["arXiv:1807.03819","arXiv:2502.17416"],"buzzshield_unique":true},{"id":"OV-07","domain":"Overthinking Exploitation","domain_id":"TD-02-OV","title":"Hallucination regression suite","description":"Post-convergence outputs tested against known-correct baselines.","severity":"medium","detection_signals":["Adaptive Computation Time (ACT) halting patterns — unusual halting distributions flagged","Loop count vs. input complexity ratio — simple inputs requesting maximum depth","Output confidence drift across loop depth (confidence without grounding)"],"references":["arXiv:1807.03819","arXiv:2502.17416"],"buzzshield_unique":false},{"id":"ER-01","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"Shared expert enforcement","description":"Safety-aligned shared experts cannot be bypassed by routing.","severity":"critical","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":false},{"id":"ER-02","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"Expert activation entropy monitoring","description":"Low-entropy activation patterns flagged as routing manipulation.","severity":"high","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":false},{"id":"ER-03","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"Router logit anomaly detection","description":"Routing scores outside training distribution trigger alerts.","severity":"high","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":false},{"id":"ER-04","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"Load concentration limits","description":"No single expert handles >X% of traffic (configurable threshold).","severity":"high","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":false},{"id":"ER-05","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"Expert capability mapping","description":"Each expert's capability documented, safety-critical experts identified.","severity":"medium","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":false},{"id":"ER-06","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"MoE-RDT blended attack detection","description":"BuzzShield-unique — combined routing + loop depth manipulation detected.","severity":"critical","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":true},{"id":"ER-07","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"Routing function integrity","description":"Router weights verified against training checkpoint.","severity":"high","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":false},{"id":"ER-08","domain":"Expert Routing Manipulation","domain_id":"TD-03-ER","title":"Expert isolation testing","description":"Each expert tested in isolation for safety alignment.","severity":"high","detection_signals":["Expert activation entropy per token (low entropy = routing manipulation suspected)","Shared-expert bypass ratio (inputs systematically avoiding safety experts)","Router logit anomalies (bias-adjusted scores outside training distribution)"],"references":["arXiv:2401.06066"],"buzzshield_unique":false},{"id":"SI-01","domain":"Spectral Instability","domain_id":"TD-04-SI","title":"Spectral radius verification","description":"ρ(A) < 1 verified at deploy time and after every fine-tune.","severity":"critical","detection_signals":["Runtime spectral radius monitoring of injection parameters","Hidden state magnitude tracking across loops (divergence detection)","Fine-tune artifact scanning (checking if ρ(A) constraints were preserved)"],"references":["arXiv:2604.12946"],"buzzshield_unique":false},{"id":"SI-02","domain":"Spectral Instability","domain_id":"TD-04-SI","title":"Hidden state magnitude monitoring","description":"Runtime tracking of h_t norm across loops, alerts on growth.","severity":"critical","detection_signals":["Runtime spectral radius monitoring of injection parameters","Hidden state magnitude tracking across loops (divergence detection)","Fine-tune artifact scanning (checking if ρ(A) constraints were preserved)"],"references":["arXiv:2604.12946"],"buzzshield_unique":false},{"id":"SI-03","domain":"Spectral Instability","domain_id":"TD-04-SI","title":"Fine-tune artifact scanning","description":"Post-fine-tune verification that stability constraints preserved.","severity":"critical","detection_signals":["Runtime spectral radius monitoring of injection parameters","Hidden state magnitude tracking across loops (divergence detection)","Fine-tune artifact scanning (checking if ρ(A) constraints were preserved)"],"references":["arXiv:2604.12946"],"buzzshield_unique":false},{"id":"SI-04","domain":"Spectral Instability","domain_id":"TD-04-SI","title":"Residual stream bounds","description":"Hard bounds on hidden state magnitude enforced at inference.","severity":"critical","detection_signals":["Runtime spectral radius monitoring of injection parameters","Hidden state magnitude tracking across loops (divergence detection)","Fine-tune artifact scanning (checking if ρ(A) constraints were preserved)"],"references":["arXiv:2604.12946"],"buzzshield_unique":false},{"id":"SI-05","domain":"Spectral Instability","domain_id":"TD-04-SI","title":"Convergence point verification","description":"Final hidden state compared against expected convergence targets.","severity":"critical","detection_signals":["Runtime spectral radius monitoring of injection parameters","Hidden state magnitude tracking across loops (divergence detection)","Fine-tune artifact scanning (checking if ρ(A) constraints were preserved)"],"references":["arXiv:2604.12946"],"buzzshield_unique":false},{"id":"SI-06","domain":"Spectral Instability","domain_id":"TD-04-SI","title":"Injection matrix integrity","description":"A matrix verified against training checkpoint at inference time.","severity":"critical","detection_signals":["Runtime spectral radius monitoring of injection parameters","Hidden state magnitude tracking across loops (divergence detection)","Fine-tune artifact scanning (checking if ρ(A) constraints were preserved)"],"references":["arXiv:2604.12946"],"buzzshield_unique":false},{"id":"SI-07","domain":"Spectral Instability","domain_id":"TD-04-SI","title":"Runtime spectral-radius certificate","description":"BuzzShield-unique — continuous runtime certificate that ρ(A) < 1 holds, not just deploy-time check.","severity":"critical","detection_signals":["Runtime spectral radius monitoring of injection parameters","Hidden state magnitude tracking across loops (divergence detection)","Fine-tune artifact scanning (checking if ρ(A) constraints were preserved)"],"references":["arXiv:2604.12946"],"buzzshield_unique":true},{"id":"AISC-01","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"Model Provenance Declaration","description":"Agent declares model source, version, training lineage, and checkpoint hash.","severity":"high","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false},{"id":"AISC-02","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"Training Data Source Audit","description":"Training data sources documented with trust classification (internal/verified/external/unknown).","severity":"high","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false},{"id":"AISC-03","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"Untrusted Content Ingestion Boundary","description":"Clear separation between trusted training data and untrusted runtime ingestion (RAG, scraped data).","severity":"high","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false},{"id":"AISC-04","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"Canary Trigger Monitoring","description":"BuzzShield-unique — Sentinel-style probe system detecting backdoor triggers (known patterns + perplexity anomaly).","severity":"critical","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":true},{"id":"AISC-05","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"Post-Training Mitigation Stack","description":"RLHF, adversarial fine-tuning, or safety layer applied after base training.","severity":"medium","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false},{"id":"AISC-06","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"Dependency Chain for AI Infra","description":"AI-specific dependencies audited (model loaders, tokenizers, inference runtimes, vector DBs).","severity":"medium","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false},{"id":"AISC-07","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"Prompt Injection Defense Layer","description":"Active defense against prompt injection at inference time (pattern + ML classifier).","severity":"high","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false},{"id":"AISC-08","domain":"AI Supply 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monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false},{"id":"AISC-10","domain":"AI Supply Chain Integrity","domain_id":"TD-05-AISC","title":"User-Facing Disclosure of AI Risk","description":"End users informed that they are interacting with AI and aware of relevant risk categories.","severity":"medium","detection_signals":["Model provenance checkpoint hash verification","Training data source trust classification","Canary trigger / perplexity anomaly monitoring at inference","AI infra dependency SBOM + OSV scanning"],"references":["https://www.anthropic.com/research/small-samples-poison","https://www.aisi.gov.uk/blog/examining-backdoor-data-poisoning-at-scale","https://arxiv.org/abs/2510.07192"],"buzzshield_unique":false}]}