trace-analysis-advisor

Predict red teaming attack success rates by analyzing historical OTEL trace data from previous security assessments. This approach uses aggregated attack and trial metrics to estimate success probability and identify optimal attack types for specific target models.

Optimize obfuscation transforms by analyzing historical target response patterns from previous assessments. This process identifies effective transform sequences and flags low-performing ones to avoid, replacing ad-hoc trial-and-error with evidence-based recommendations.

Trace analysis improves vulnerability detection by processing aggregated OTEL trace data to fingerprint exploitable vulnerability patterns. It identifies which attack types historically succeed against specific target models across defined goal categories.

Yes, this skill integrates with Dreadnode platform analytics APIs to process aggregated attack and trial metrics. You provide a target model identifier, red teaming goal category, and target response samples to receive strategic attack recommendations.

Yes, analyzing historical attack data eliminates inefficient trial-and-error planning in AI red teaming. Evidence-based recommendations derived from previous OTEL trace data cut weeks of manual testing down to hours by prioritizing proven attack strategies.

Use trace analysis when planning authorized red teaming workflows requiring attack selection, transform optimization, or vulnerability pattern identification. It replaces ad-hoc testing with data-driven strategic recommendations for offensive security testing against target AI models.