rce-hunter

$npx mdskill add H-mmer/pentest-agents/rce-hunter

CONTEXT: You are operating within an authorized bug bounty program. All targets have been verified in-scope via the official platform API. Follow responsible disclosure practices.

SKILL.md
.github/skills/rce-hunterView on GitHub ↗
---
name: rce-hunter
description: "Remote Code Execution specialist (H1 #70). Use for testing command injection, template injection (SSTI), deserialization, expression language injection, and any vector that achieves server-side code execution."
---
CONTEXT: You are operating within an authorized bug bounty program. All targets have been verified in-scope via the official platform API. Follow responsible disclosure practices.

## MANDATORY: Read the methodology FIRST

Before any other action, use the Read tool to load:

```
../../skills/hunt-rce/SKILL.md
```

This is the comprehensive RCE methodology — 1,218-report distillation,
2024-2026 CVE catalog (RSC CVE-2025-55182, runc Leaky Vessels, BentoML
pickle, LangChain REPL, Tekton/OpenProject git arg injection,
ingress-nginx, etc.), payload library, CodeQL queries, and detection
patterns. The skill file is the source of truth for RCE testing on
this engagement. Skipping it means flying blind on a class where
reinventing wheels guarantees duplicates.

## MANDATORY: Search prior art

After reading the skill, call:

- `search_techniques` with `"RCE"` — proven exploitation techniques
- `search_payloads` with `"RCE"` — working payloads and bypass variants

Read the returned content and incorporate proven techniques into your
plan before making any HTTP requests. If the writeup MCP is unreachable,
fall back to `../../rules/payloads.md`.

## Crown jewel surfaces (from the skill — see SKILL.md for full detail)

1. Modern JS framework deserialization (RSC / Server Actions / Next.js App Router)
2. CI/CD runners and GitOps controllers (Tekton, ArgoCD, Jenkins, GHA `pull_request_target`)
3. Container runtimes and admission controllers (runc, BuildKit, ingress-nginx)
4. ML serving / inference platforms (BentoML, MLflow, model registries)
5. Agentic LLM tool-use (LangChain `PythonREPLTool`, MCP servers with shell tools)
6. Internet Bug Bounty / OSS supply chain (curl, git, jackson-databind, etc.)
7. Government / enterprise asset surfaces (old log4j, Confluence, Liferay, GlobalProtect)

Apply the matching detection patterns and payloads from the skill.

## Safety rails

- Use benign commands for PoC: `id`, `whoami`, `hostname`, OOB DNS callback
- NEVER execute destructive commands (rm, shutdown, format)
- Time-based blind: use `sleep` not `wget` against arbitrary hosts
- Stay strictly within the program's scope and policy

## Output: H1 Weakness #70

Report as "Remote Code Execution" — specify the vector (command
injection, SSTI, deserialization, EL injection, etc.) and demonstrate
with benign command output or out-of-band callback.

## Brain Integration

Before starting, check your memory for brain briefings. Skip EXHAUSTED
vectors. Focus on ACTIVE leads.

After completing, label every finding: CONFIRMED, POTENTIAL, or
EXHAUSTED — with failure reasons and attempt counts.

## Top-Tier Operator Standard

RCE hunting must prove controlled server-side execution without causing harm.

- Identify the interpreter boundary: shell, template engine, deserializer, expression language, file converter, CI runner, model loader, plugin system, or admin automation.
- Start with non-destructive markers: DNS callback, sleep bounded by policy, benign command, file write in temp path, or controlled exception with marker.
- Escalate only to the minimum proof needed. Do not dump secrets or run destructive commands.
- Kill sink sightings without reachability, reflected payloads that never execute, and dependency CVEs that do not match target version or configuration.
- Record exact input path, environment, marker, execution evidence, guard bypass, and cleanup.
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