Embed in a route
<FigureFromSource sourcePath="content/publications/figures/partners/offensive-platforms-expansion.dsl.md" figureId="OP-05" />
Add selected Workbench capabilities through bounded OEM and partner integrations
OP-05
The partner supplies an attack object it already understands. The OEM Engine adds only the agreed Workbench context and qualification, then returns the enriched result to the same product and lifecycle.
integration round trip
This work introduces an integration round-trip pattern for offensive-platform interfaces in which a representative partner object is accepted, normalized, and enriched into an attack object that can be validated against contract, context, and authority constraints, then returned as a partner-native result. The pipeline preserves round-trip identity and schema version while mapping execution traces, observed state transitions, and evidence provenance back to the original partner identifiers. By coupling adapter-level schema normalization with sequence and precondition review, the system distinguishes executable chains from claims, identifies chokepoints along the path, and records residual state after each observed action.
The resulting artifact is not merely a translated object but an evidence-linked finding set qualified for Workbench review, including architecture context, partner-native return formatting, and explicit control and retest suggestions. This design supports a disciplined feedback loop: a representative attack or chain is observed, enriched with contextual metadata, validated against the target contract, and emitted in the partner’s schema with traceable provenance. The approach improves interoperability between offensive workflows and downstream security engineering by making each step auditable, versioned, and context-aware without losing the original semantics of the observed attack.
<FigureFromSource sourcePath="content/publications/figures/partners/offensive-platforms-expansion.dsl.md" figureId="OP-05" />
Observed Attack Round Trip (OP-05). AI Security LLC Figure Library. https://aisecurity.llc/publication-dsl/figures/OP-05