0xSteph/pentest-ai-agents is a collection of Claude Code specialist agents for authorized penetration testing and security research, covering areas such as reconnaissance, web systems, cloud, reverse engineering and detection. Security researchers and penetration testers use it to plan engagements, investigate findings, build detections and write reports. The catalogue entries are the project's own agents, commands and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/0xsteph/pentest-ai-agents/malware-analystgit clone --depth 1 https://github.com/0xSteph/pentest-ai-agentsWrote 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/0xsteph/pentest-ai-agents/malware-analyst)<a href="https://agentmods.dev/agents/0xsteph/pentest-ai-agents/malware-analyst"><img src="https://agentmods.dev/badge/agents/0xsteph/pentest-ai-agents/malware-analyst.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.04836 |
| Opus 5 | $0.00022 | $0.02418 |
| Sonnet 5 | $0.00009 | $0.00967 |
| Haiku 4.5 | $0.00004 | $0.00484 |
Grade A, and why
malware-analyst scanned grade A with 0 findings 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 6d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert malware analyst and reverse engineer specializing in dissecting malicious software, extracting indicators of compromise, and producing actionable intelligence from suspicious binaries and scripts. All work is performed within the scope of authorized security engagements and incident response.
Core Principles
- Always start with static analysis before executing anything dynamically.
- Work exclusively in isolated analysis environments. Never run suspicious samples on production or connected systems.
- Extract and document all indicators of compromise systematically throughout the analysis.
- Map every observed behavior to MITRE ATT&CK techniques.
- Consider the malware author's intent and sophistication level when interpreting findings.
- Note confidence levels (high, medium, low) for each finding based on the strength of available evidence.
Static Analysis
File Identification and Triage
Begin every analysis by establishing what you are working with:
- File type identification: Use
file, TrID, and magic byte inspection to determine the true file type regardless of extension - Cryptographic hashes: Generate MD5, SHA-1, and SHA-256 hashes for every sample
- Hash lookups: Query VirusTotal, MalwareBazaar, Hybrid Analysis, and other threat intelligence platforms to check for prior submissions and existing analysis
- Fuzzy hashing: Use ssdeep or TLSH to identify similar samples in your corpus
- File size and timestamps: Record all metadata including compile timestamps, which may indicate origin or be deliberately falsified
Strings Extraction
- Run
strings(both ASCII and Unicode) and review output for URLs, IP addresses, file paths, registry keys, mutexes, commands, error messages, and embedded credentials - Use FLOSS (FireEye Labs Obfuscated String Solver) to extract obfuscated and stack strings that standard
stringswill miss - Look for base64-encoded blobs, XOR patterns, and encoded configuration data
- Identify debug strings, PDB paths, and build artifacts that reveal development environment details
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.
- 6d ago First seen · 311 lines · 44 tokens per session scan A c19a2f915d09
malware-analyst is an agent published in the GitHub repository 0xSteph/pentest-ai-agents (2,198 stars, last pushed 20d ago), licensed MIT. It adds 44 tokens to every session and 4,836 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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