Sprint 033 — Engineering Report: Full-Spectrum Security Assessment Validation

Sprint: 033 Wave: 18 Theme: Integration validation — prove HunterX v7 runs complete security-assessment missions across seven classes as an integrated platform, not a collection of scanners. Date: 2026-08-10

1. Executive summary

Sprint 033 validates the integrated HunterX v7 architecture end-to-end. Seven full-spectrum mission classes (bug bounty, web pentest, API security, external attack surface, cloud/SaaS, vulnerability research and red-team recon) were driven through the autonomous mission orchestrator against synthetic targets. Each mission exercised the complete DISCOVER → … → REPORT → REASSESS loop, and each produced validated, independently-proven findings while rejecting the injected false positives. A new operator-visibility surface (dashboard API + hunterx hunt CLI) and golden datasets were added. No parallel architecture was introduced; the sprint reused the existing mission orchestration, adaptive planning, tool integration SDK, finding/proof/validation lifecycle and TIDB.

2. Architecture impact

The sprint made minimal, additive changes to the existing architecture:

1. MissionDashboardService (src/hunterx/application/mission_dashboard.py) — a read-only projection service that composes the orchestration engine and the TIDB query service into operator-facing dashboard views. It delegates to existing services; it does not duplicate the mission domain.
2. Dashboard API router (src/hunterx/api/mission_dashboard.py) — adds the endpoints the orchestration router did not already expose (/overview, /attack-surface, /evidence, /proofs, /tools). Coverage, hypotheses, findings, attack paths and timeline were already served by the /missions group and were not re-registered.
3. CLI hunt group (src/hunterx/cli/commands.py) — hunterx hunt plus status/surface/coverage/findings/evidence/proofs/paths/timeline, wrapping the orchestration and dashboard services.
4. Finding-cascade rule for authorization_bypass added to the existing FindingCascadeEngine — no new engine, one deterministic rule.
5. Composition root wiring for the dashboard service in platform.py and assembler.py.

No engine, tool adapter, parser, normalizer, or persistence model was re-implemented.

3. Implemented components

4. Integration coverage

The sprint validated the wiring of every named subsystem without re-implementing it:

• Mission planning / orchestration: objectives bug_bounty_hunt, web_application_assessment, api_assessment, external_attack_surface, cloud_assessment, vulnerability_research, red_team_assessment map to the ratified planning objectives via resolve_objective.
• Tool Integration SDK: tool executions ingested through MissionOrchestrator.ingest_result → normalized observations → hypothesis loop → coverage → findings.
• Hypothesis / evidence / finding / proof engines: exercised by every mission (SQLi/IDOR/SSRF/SSTI/etc. validated and proven; false positives rejected).
• Target intelligence / memory: TIDB records persisted through MissionOrchestrationService _persist_* helpers; dashboard records() reads them back.
• Knowledge graph / attack paths: the external attack-surface mission builds the unified AttackSurfaceGraph and discovers multi-step attack paths via the adaptive planning engine.
• Reporting: the ReportabilityEngine / FindingQualityEngine gates remain the report gate; missions finalize to REPORTING.

5. Tool coverage

The toolchain is integrated at three levels, all of which were exercised or covered by the validation matrix:

1. TIP knowledge + capability selection — subdomain, DNS, live host, tech, web, content, JS, secrets, auth, authorization, cloud, vulnerability tool families registered in the composition root.
2. Execution SDK adapters — httpx, katana, nmap, naabu, dnsx, ffuf, nuclei, gitleaks, proof-replay, safe-validation probes, etc.
3. Mission-level tool selection — the orchestrator selects tools per capability (subfinder for subdomain_enumeration, sqlmap for sql_injection, interactsh for ssrf, …) and records every execution on the mission context and TIDB.

Binary-dependent tools (Amass, Masscan, SQLmap, Interactsh, ZAP, mitmproxy, Metasploit, …) are exercised via synthetic outputs in the deterministic acceptance/golden suites (their parsers/adapters are wired; live execution requires installation and is outside the deterministic CI surface).

6. Mission coverage

7. Finding validation

Every finding advanced through the evidence-gated lifecycle: CANDIDATE → SUPPORTED → VALIDATING → VALIDATED → PROVING → PROVED → REPORT_READY. Transitions required the declared evidence purposes (HYPOTHESIS/VALIDATION/PROOF/REPORTING) enforced by FindingLifecycleStateMachine. The mission-level FindingStage mirrored this (candidate → supported → verified → proven → report_ready). Validated findings always carried: affected asset, evidence refs, tool provenance, confidence and impact analysis.

8. PoC validation

Proof/PoC generation and replay are handled by the existing SafeProofGenerator and ReplayEngine (domain/vulnerability_proof/) plus the proof-replay tool adapter. The missions recorded proved coverage cells with evidence refs and replayable proof; the golden datasets assert proof_cells_gte. Replayability is demonstrated deterministically through the golden replay test and the checkpoint/resume path in the orchestration engine.

9. False-positive performance

Injected false positives and decoys were all rejected or retained as negative evidence: inert XSS reflection, uncorroborated privilege-escalation claims, unreachable schema endpoints, fake secrets, decoy files and ambiguous webhook signatures. DETECTION != VALIDATION held across every mission.

10. Failure recovery

The amass crash in the bug-bounty mission was classified as blocked negative evidence; the mission continued and the corroborating tool (assetfinder) provided the same subdomains. Tool substitution is capability-based via ToolFallbackResolver (Nmap → Naabu/Masscan/RustScan), never a hardcoded replacement.

11. Performance

The deterministic missions complete in under a second each (test_bug_bounty_mission ≈ 0.4 s, the attack-surface mission ≈ 5.4 s including a fresh platform build for attack-path discovery). The dashboard projections are O(n) over the mission aggregate and add negligible latency. Full platform build stays within the existing performance benchmarks (tests/performance/).

12. Security

Cross-target contamination, evidence contamination, malformed output, oversized output and mission-state corruption are covered by the existing security suite (tests/security/), which remains green. The dashboard exposes read-only projections and never mutates mission state. No secrets are logged; the mission-dashboard views aggregate normalized data only.

13. Test results

tests/acceptance/full_assessment/         7 passed   (7 mission classes)
tests/golden/full_assessment/             7 datasets + replay + determinism
tests/golden/test_full_assessment_golden.py  9 passed
tests/integration/test_mission_dashboard_api.py 6 passed
tests/integration/test_mission_dashboard_cli.py 4 passed
existing orchestration/api/cli/golden suites  green (regression)

All touched files pass ruff; the full v7 suite remains green.

14. Known limitations

• Deterministic suites exercise tools via synthetic outputs; live validation of Amass/Masscan/SQLmap/Interactsh/ZAP/mitmproxy/Metasploit requires those binaries installed and is not part of the CI surface.
• The MissionDashboardService.records() view depends on the TIDB query service; with in-memory stores it reflects the current process only.
• The external attack-surface and red-team missions demonstrate attack-path discovery through the adaptive planning engine with a separately constructed graph; automatic graph construction from all capability outputs remains an ongoing integration point.

15. Technical debt

• test_attack_surface_mission.py builds a fresh platform for attack-path discovery; sharing one platform fixture across the seven missions would reduce wall-clock time.
• The dashboard router currently adds only the five endpoints not already in the orchestration router; a future consolidation could unify the two /missions route groups.
• Golden datasets repeat the expected_* metadata contract; a shared schema validator would enforce it uniformly.

16. Next recommendations

1. Wire the attack-surface graph construction directly from mission capability outputs so attack-path discovery runs inside the orchestrator loop.
2. Add a live-tool integration suite (pytest tools marker) that executes the installed binaries against disposable targets and validates the real parsers/normalizers.
3. Consolidate the /missions route groups into a single router surface.
4. Persist mission dashboards to TIDB so operator views survive process restarts without the in-memory engine.
5. Extend the false-positive lab with additional contradictory tool pairs and adversarial parser inputs.