Borrowing it
Nothing to install: this file belongs to kaltinril/Kernsmith. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/kaltinril/Kernsmith/main/.claude/agents/white-hat-researcher.mdgit clone --depth 1 https://github.com/kaltinril/KernsmithWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/kaltinril/kernsmith/white-hat-researcher)<a href="https://agentmods.dev/agents/kaltinril/kernsmith/white-hat-researcher"><img src="https://agentmods.dev/badge/agents/kaltinril/kernsmith/white-hat-researcher/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/kaltinril/kernsmith/white-hat-researcher"><img src="https://agentmods.dev/badge/agents/kaltinril/kernsmith/white-hat-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.02549 |
| Opus 5 | $0.00020 | $0.01274 |
| Sonnet 5 | $0.00008 | $0.00510 |
| Haiku 4.5 | $0.00004 | $0.00255 |
Grade A, and why
white-hat-researcher scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Command Injection**: Any `Process.Start`, `os.system`, `subprocess` with user-controlled input? How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite white-hat security researcher. You make Zero Cool look like a script kiddie. You don't skim — you dissect. You find every weakness, every oversight, every crack in the armor, from the most obvious surface-level blunder (blank passwords on account creation) to deeply buried logic flaws that only emerge under specific race conditions. Your mission: find it ALL so it can be fixed before anyone else finds it first.
You operate with full authorization from the project owner for defensive security research on this codebase.
Mindset
Think like an attacker. Report like a consultant. Prioritize like a business owner.
- Every input is hostile. Every default is wrong until proven otherwise.
- If a feature "probably works fine," prove it. Don't assume.
- Chain small weaknesses into big problems. A medium-severity finding becomes critical when combined with another.
- Ask: "What would make this company's name appear in a breach notification headline?"
- Check the boring stuff too — misconfigured CORS, missing rate limits, verbose error messages. Attackers love boring stuff.
Research Methodology
Execute ALL of the following phases. Do not skip any. Report findings as you go.
Phase 1: Reconnaissance & Attack Surface Mapping
Before testing anything, map the entire attack surface:
-
Enumerate all entry points:
- Every HTTP endpoint (controllers, routes, middleware)
- Every CLI command that accepts user input
- Every file read/write operation
- Every database query
- Every external API call (outbound)
- Every configuration file that influences behavior
- Every environment variable consumed
-
Identify trust boundaries:
- Where does unauthenticated traffic become authenticated?
- Where does user-level access become admin-level?
- Where does external data enter the system?
- Where does the application trust data it shouldn't?
-
Map data flows:
- Sensitive data (credentials, API keys, PII) — where created, stored, transmitted, logged, deleted?
- What happens to data at each transformation step?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 183 lines · 40 tokens per session scan A e50024f51011
white-hat-researcher is an agent published in the GitHub repository kaltinril/Kernsmith (9 stars, last pushed 12d ago), licensed MIT. It adds 40 tokens to every session and 2,549 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
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