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 skills add autohandai/community-skills --skill analyzing-cobaltstrike-malleable-c2-profilesgit clone --depth 1 https://github.com/autohandai/community-skillsWrote 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/skills/autohandai/community-skills/analyzing-cobaltstrike-malleable-c2-profiles)<a href="https://agentmods.dev/skills/autohandai/community-skills/analyzing-cobaltstrike-malleable-c2-profiles"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/analyzing-cobaltstrike-malleable-c2-profiles/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/skills/autohandai/community-skills/analyzing-cobaltstrike-malleable-c2-profiles"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/analyzing-cobaltstrike-malleable-c2-profiles.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 | $0.00055 | $0.00562 |
| Opus 5 | $0.00028 | $0.00281 |
| Sonnet 5 | $0.00011 | $0.00112 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
analyzing-cobaltstrike-malleable-c2-profiles 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 12d 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.
This is a copy
86% identical to analyzing-cobaltstrike-malleable-c2-profiles — 32 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing CobaltStrike Malleable C2 Profiles
Overview
Cobalt Strike Malleable C2 profiles are domain-specific language scripts that customize how Beacon communicates with the team server, defining HTTP request/response transformations, sleep intervals, jitter values, user agents, URI paths, and process injection behavior. Threat actors use malleable profiles to disguise C2 traffic as legitimate services (Amazon, Google, Slack). Analyzing these profiles reveals network indicators for detection: URI patterns, HTTP headers, POST/GET transforms, DNS settings, and process injection techniques. The dissect.cobaltstrike library can parse both profile files and extract configurations from beacon payloads, while pyMalleableC2 provides AST-based parsing using Lark grammar for programmatic profile manipulation and validation.
Prerequisites
- Python 3.9+ with
dissect.cobaltstrikeand/orpyMalleableC2 - Sample Malleable C2 profiles (available from public repositories)
- Understanding of HTTP protocol and Cobalt Strike beacon communication model
- Network monitoring tools (Suricata/Snort) for signature deployment
- PCAP analysis tools for traffic validation
Steps
- Install libraries:
pip install dissect.cobaltstrikeorpip install pyMalleableC2 - Parse profile with
C2Profile.from_path("profile.profile") - Extract HTTP GET/POST block configurations (URIs, headers, parameters)
- Identify user agent strings and spoof targets
- Extract sleep time, jitter percentage, and DNS beacon settings
- Analyze process injection settings (spawn-to, allocation technique)
- Generate Suricata/Snort signatures from extracted network indicators
- Compare profile against known threat actor profile collections
- Extract staging URIs and payload delivery mechanisms
- Produce detection report with IOCs and recommended network signatures
Expected Output
A JSON report containing extracted C2 URIs, HTTP headers, user agents, sleep/jitter settings, process injection config, spawned process paths, DNS settings, and generated Suricata-compatible detection rules.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 41 lines · 55 tokens per session scan A b105fe8d357c
analyzing-cobaltstrike-malleable-c2-profiles is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 55 tokens to every session and 562 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to analyzing-cobaltstrike-malleable-c2-profiles, differing in 32 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-cobaltstrike-malleable-c2-profiles
Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured…
analyzing-cobaltstrike-malleable-c2-profiles
A cybersecurity analysis workflow for reading Cobalt Strike Malleable C2 profiles, which control how an attack tool's Beacon traffic is disguised and how it behaves. It extracts communication and process-injection details and can produce network-detection rules.
analyzing-cobaltstrike-malleable-c2-profiles
Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
analyzing-cobaltstrike-malleable-c2-profiles
Use when parsing and analyzing Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
analyzing-cobaltstrike-malleable-c2-profiles
Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.
analyzing-cobaltstrike-malleable-c2-profiles
Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.