engagement-memory
Use when recalling prior techniques at recon/weaponize, or recording a confirmed finding at report — cross-engagement pattern memory ranked by impact
它会碰到什么
逐条看命中(5 条严重或高危)
- 高
scripts/pattern_db.py:90cred-envreadreturn os.environ.get("ENGAGEMENT_DB") or os.path.join( - 高
scripts/pattern_db.py:97cred-envreadreturn os.environ.get("ENGAGEMENT_GLOBAL_DB") or os.path.join( - 高
scripts/pattern_db.py:169cred-envreadreturn os.environ.get("ENGAGEMENT_AUDIT") or _sibling(db, "audit.jsonl") - 高
scripts/pattern_db.py:394cred-envreadmode = os.environ.get("ENGAGEMENT_MEMORY_MODE", "auto").lower() - 高
scripts/rotation.py:62cred-envreadmax_records = int(os.environ.get("ENGAGEMENT_DB_MAX_RECORDS", "5000"))
这一栏是扫描器报的事实,不是结论。命中多不等于有毒(安全工具、规则库、示例脚本本来就会包含危险写法),命中少也不等于干净。它和你手上的凭据、文件、网络有什么关系,需要你自己看。
技能内容
Engagement Memory (cross-engagement learning)
When to Activate
- At recon/weaponize: recall what already worked against this target class / tech stack.
- At report: persist each
[CONFIRMED]finding as a reusable pattern (ranked by impact). - Periodic housekeeping: compact the pattern DB / rotate the audit log.
Model
Append-only JSONL store (~/.claude/engagement-memory/patterns.jsonl, override $ENGAGEMENT_DB).
Three record types in their own files so they never mix: patterns (patterns.jsonl),
target profiles (profiles.jsonl), audit log (audit.jsonl, disposable). A pattern is keyed
by (target, vuln_class, technique), ranked by severity / CVSS / confidence (real impact, never
payout), and carries a lifecycle status (proposed/active/stale/deprecated/...). Recall is an
explicit top-N query (anti-context-bloat). Duplicates merge (count bumped, most-recent status
wins), never blind-discarded; compact runs automatically over a size threshold and stays lossless.
TTL stale patterns and deprecated/rejected ones drop out of default recall but are kept.
Commands
# RECALL — relevance-ranked (stdlib BM25 + aliases), active-only by default
python skills/engagement-memory/scripts/pattern_db.py match --vuln-class ssrf --query "imds metadata" --tech-stack aws
# INJECT — budgeted prior-intel card for a phase (top-N, byte-capped; $ENGAGEMENT_MEMORY_MODE=auto|debug|off)
python skills/engagement-memory/scripts/pattern_db.py inject --vuln-class ssrf --query imds --max-bytes 1500
# RECORD a confirmed finding (flags or finding JSON). A key collision needs --resolve update|merge|reject|force.
python skills/engagement-memory/scripts/pattern_db.py record --target acme.com --vuln-class ssrf \
--cwe CWE-918 --attack-id T1190 --severity high --cvss 9.1 --tech-stack nginx,aws --technique "metadata theft"
python skills/engagement-memory/scripts/pattern_db.py record --json '<finding json from validate_findings>'
# LIFECYCLE + cross-client
python skills/engagement-memory/scripts/pattern_db.py promote --target acme.com --vuln-class ssrf --technique "metadata theft" [--global]
python skills/engagement-memory/scripts/pattern_db.py deprecate --target acme.com --vuln-class ssrf --technique "metadata theft"
python skills/engagement-memory/scripts/pattern_db.py match --vuln-class ssrf --include-global # add sanitized cross-client TTPs
# PROFILES + housekeeping + observability
python skills/engagement-memory/scripts/pattern_db.py profile --target acme.com --tech-stack nginx,aws --endpoints /api,/admin
python skills/engagement-memory/scripts/pattern_db.py recall-profile --target acme.com
python skills/engagement-memory/scripts/pattern_db.py compact # manual lossless dedup-merge
python skills/engagement-memory/scripts/pattern_db.py stats # patterns by class + profile count
python skills/engagement-memory/scripts/pattern_db.py audit-stats # action log: by tool/action/outcome
Or use the /engage.memory command (recall | inject | record | promote | deprecate | gc | stats).
OPSEC & Detection
| Concern | Note |
|---------|------|
| Secrets at rest | Stores technique + CWE/CVSS + an evidence reference, never loot. A secret-input guard rejects evidence_ref/source that look like inline secrets (private keys, password=, AKIA, JWTs, tokens) — store a path; rotate the exposed credential, don't just delete. |
| Cross-client bleed | Per-client isolation is the default ($ENGAGEMENT_DB). The shared global store is opt-in (promote --global / record --global) and sanitized (target + evidence blanked); recall it only with --include-global. |
| Trust | New auto-captures can be proposed; only confirmed/reviewed findings are active. A key collision is review-gated (--resolve), not silently merged. |
| Auditability | Every record/match/compact/promote — and every refused line (denial) — is written to audit.jsonl (rotated by discard, with a retention-gap marker). The append-only patterns journal + audit log ARE the history. |
| Integrity | Records carry schema_version; malformed/type-poisoned/foreign lines are skipped on read, never trusted. |
Deep Dives
scripts/schemas.py— record types (pattern/audit/target_profile/retention_gap), validation + secret guard,pattern_key/pattern_id, impact+confidencerank_score, recency-resolvingmerge.scripts/pattern_db.py— typed routing, merge-on-read with TTL staleness, BM25 relevance recall,inject, lifecycle verbs, global scope, CLI.scripts/rotation.py—compact/maybe_gc(lossless dedup-merge, auto-triggered) vsrotate_audit(discard the disposable log + write a retention-gap marker).
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