attack-vector-generation
This Claude Code skill generates specific, executable attack vectors targeting identified threat surfaces by spawning a specialized subagent for adversarial ideation. Use it during security research and penetration testing workflows when you need concrete attack strategies tailored to particular vulnerabilities, with outputs ranked by impact severity and conditions for escalation.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/attack-vector-generation && cp -r /tmp/attack-vector-generation/skills/attack-vector-generation ~/.claude/skills/attack-vector-generationSKILL.md
# Attack Vector Generation Generates concrete, executable attack vectors targeting a specific threat surface. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Why Subagent Vector generation requires creative adversarial thinking without defensive contamination. Each surface needs fresh attack ideation. ## Input - **surface**: The specific threat surface to target (from threat-surface-mapping) - **artifact**: The artifact being attacked - **prior_findings**: Results from previous probes (to avoid repetition) ## Output - **vectors**: List of specific attack vectors with description, expected outcome, and severity estimate - **priority_order**: Recommended execution order (highest-impact first) - **follow_up_triggers**: Conditions that should trigger deeper investigation
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: 面对异常的最佳解释推理
Remove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems