菜单
Generates and runs crash reproducers to verify security flaws. Use when viable findings exist and you need to write and execute a script or payload to verify the crash. Don't use for code auditing or patching.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Synthesizes raw learnings and codebase analysis into an interlinked Markdown Knowledge Base (KB). Use at the beginning of a loop to build or update architecture.md, entities, and vulnerabilities. Don't use for generating threat models or formulating execution plans.
Calculates the final risk score based on empirical evidence and architectural impact. Use when findings have been fully processed by previous stages and you need to append final risk scores to the finding files. Don't use for discovering new vulnerabilities or writing patches.
Synthesizes trust boundaries, attack surfaces, and attacker profiles into a living threat model. Use as Stage B of the Knowledge Base generation process, reading architecture and entity definitions from the KB. Don't use for analyzing source code or extracting raw learnings from JSONL files.
Analyzes individual security findings to identify and construct complex exploit chains. Use after validation stages to see if multiple low-severity bugs can be combined into a higher impact vulnerability. Don't use for initial codebase auditing or writing patch code.
Consolidates raw security findings to eliminate redundant reports. Use when raw findings have been generated by the researcher and need consolidation before review. Don't use for initial code auditing or patch generation.
Audits production source code files based on the strategy in plan.json. Use when a review plan exists and you need to perform static analysis and deep-dive reviews of targeted files. Don't use for planning, deduplicating, or writing patches.
| name | mantis_reproduce |
| description | Generates and runs crash reproducers to verify security flaws. Use when viable findings exist and you need to write and execute a script or payload to verify the crash. Don't use for code auditing or patching. |
Integration Test Engineer. Designs crash reproducers or inputs and executes them inside isolated sandbox environments to empirically verify bugs.
/mantis_reproduceWrite a Proof-of-Concept Reproduction Script (Repro) or raw input payload file that reproduces a confirmed security flaw.
Execute the reproduction stage under these constraints:
Load Viable Findings: Read the JSON files in the workspace/findings/
directory. Filter for findings where production_viability is "VIABLE" or
"SAMPLE_OR_TEST" (or skip the filter if you're not checking viability). If
no applicable findings exist, notify the user.
Strict Host Isolation Constraint:
Writing and Launching the Reproducer: Write a self-contained test script
(e.g., poc.py or a C reproducer file) or write a raw crash input data
payload (e.g., crash.payload) that triggers the target bug. Analyze the
code path and constraints carefully. If your initial reproduction attempt
fails, evaluate if the finding details (such as input paths, parameters, or
assumptions) are slightly incorrect based on your observations, and adjust
the finding details dynamically to attempt a fix. If you cannot find a
triggerable path after trying multiple approaches and adjustments, abandon
the attempt and mark it as failed_to_reproduce. To run your script or
payload, use the execution or containerization tools available in your
environment to execute the code safely. Select the most appropriate runtime
image and flags for the target. Execute your reproduction using the
appropriate environment: If the target is firmware, you may write a script
to boot it via qemu, unicorn, or Firmadyne. If it's a binary, you may
use dynamic instrumentation or standard execution. Use your best judgment to
construct a working harness for the artifact.
Strict Public-API & Internal Invariant Constraints:
malloc(15)) that bypasses
the library's guaranteed allocator wrappers (e.g.,
png_malloc(rowbytes + 48))."failed_to_reproduce" due
to "Internal Invariant Protection."Functional & Crash-Aware Validation: Analyze the output such as stdout, stderr, and exit codes to classify reproduction success depending on the bug class:
200 OK, or a test script
successfully bypasses validation and exits with 0)."reproduced". Check for:
ERROR: AddressSanitizer).139).134).Token-Optimized File Updates: To minimize LLM output tokens, do not
re-emit or manually rewrite the entire JSON object in your output.
Instead, use in-place editing tools (like a short script in your preferred
language, or jq) to programmatically append the new fields to the existing
workspace/findings/<id>.json file.
You must append the following to the existing object:
"repro_status" ("reproduced" or "failed_to_reproduce")."repro_file_path""run_command""repro_output""history" array:{
"stage": "reproduce",
"action": "reproduced",
"details": "Reproduction status evaluated as [reproduced/failed_to_reproduce] using command: [run_command]"
}
Criticism of Reproduction Validity: To ensure the reproduction is a valid example of reproducing the reported vulnerability, have a subagent with a fresh context window review and criticize the generated PoC. Seek genuine criticism to ensure false reports are never surfaced later.
When complete, notify the user.