rce-hunter
SkillSecurityRemote 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the rce-hunter skill
What this skill tells your AI
The instructions your AI receives, as published by h-mmer/pentest-agents in providers/cursor/.cursor/skills/agent-rce-hunter/SKILL.md and read by ahel’s review.
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_techniqueswith"RCE"— proven exploitation techniquessearch_payloadswith"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)
- Modern JS framework deserialization (RSC / Server Actions / Next.js App Router)
- CI/CD runners and GitOps controllers (Tekton, ArgoCD, Jenkins, GHA
pull_request_target) - Container runtimes and admission controllers (runc, BuildKit, ingress-nginx)
- ML serving / inference platforms (BentoML, MLflow, model registries)
- Agentic LLM tool-use (LangChain
PythonREPLTool, MCP servers with shell tools) - Internet Bug Bounty / OSS supply chain (curl, git, jackson-databind, etc.)
- 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
sleepnotwgetagainst 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.
Signals
- GitHub stars
- 908
- Forks
- 169
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
rce-hunter- Source
- github.com/h-mmer/pentest-agents