external/trailofbits-security/trailmark/skills/trailmark-variant-neighborhood/SKILL.md
Expands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.
npx skillsauth add seikaikyo/dash-skills trailmark-variant-neighborhoodInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Expand one seed issue into graph-derived variant candidates. This skill generates review targets, not confirmed findings.
variant-analysis, semgrep-rule-creator,
static-analysis, or manual reviewsemgrep-rule-creator directly.| Rationalization | Why It Is Wrong | Required Action | |---|---|---| | "Nearby code means variant" | Proximity is only a candidate reason | Rank it as a review target | | "Only exact same names matter" | Variants often share sinks or preconditions, not names | Expand across callers, callees, interfaces, and types | | "Every candidate is a finding" | This skill outputs candidates for review | Avoid vulnerability claims | | "Unreachable candidates can be ignored completely" | They may become reachable after refactors | Rank lower or list as deferred | | "Graph candidates replace semantic pattern work" | Graph structure finds locations, not root-cause semantics | Hand off to variant-analysis, Semgrep, CodeQL, or manual review |
Variant Neighborhood Progress:
- [ ] Step 1: Normalize and bind the seed
- [ ] Step 2: Expand graph neighborhoods
- [ ] Step 3: Rank candidates
- [ ] Step 4: Extract variant pattern guidance
- [ ] Step 5: Emit handoff packet
Accept finding text, file/line, function name, or output from
trailmark-finding-triage. Bind the seed to a Trailmark node and record the
root cause in plain language.
If the seed has no concrete graph binding, stop before inventing variants.
Use the dimensions in references/neighborhood-patterns.md:
Bound expansion to avoid candidate floods.
Rank with references/ranking.md. Prioritize entrypoint-reachable, tainted, boundary-adjacent, high-blast-radius, shared sink, same-interface, and close-distance candidates. Penalize test, mock, generated, vendor, unreachable, and trusted-internal-only candidates.
Summarize what should be searched for syntactically and what requires semantic review. Identify whether follow-up belongs in:
variant-analysissemgrep-rule-creatorstatic-analysis with CodeQL or SARIF-producing toolsUse references/output-format.md. Include ranked candidates, inclusion reasons, exclusions, limitations, and the variant-analysis handoff.
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.