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 oyi77/1ai-skills --skill analyzing-cobaltstrike-malleable-c2-profilesgit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/analyzing-cobaltstrike-malleable-c2-profiles)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-cobaltstrike-malleable-c2-profiles"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-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/oyi77/1ai-skills/analyzing-cobaltstrike-malleable-c2-profiles"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-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 | $0.00057 | $0.01131 |
| Opus 5 | $0.00028 | $0.00566 |
| Sonnet 5 | $0.00011 | $0.00226 |
| Haiku 4.5 | $0.00006 | $0.00113 |
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 8d 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
89% identical to analyzing-cobaltstrike-malleable-c2-profiles — 67 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 — 118 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.
When to Use
Trigger phrases:
-
"analyzing cobaltstrike malleable c2 profiles"
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"Parse and analyze Cobalt Strike Malleable C2 profiles using dissect"
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When investigating security incidents that require analyzing cobaltstrike malleable c2 profiles
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When building detection rules or threat hunting queries for this domain
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When SOC analysts need structured procedures for this analysis type
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When validating security monitoring coverage for related attack techniques
When NOT to Use
- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope
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
# Example: IOC detection
import re
IOC_PATTERNS = {
"ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
"domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
"hash_md5": r"\b[a-f0-9]{32}\b",
"hash_sha256": r"\b[a-f0-9]{64}\b",
}
def extract_iocs(text: str) -> dict:
return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
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.
- 8d ago First seen · 118 lines · 57 tokens per session scan A 102c98852a30
analyzing-cobaltstrike-malleable-c2-profiles is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,131 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to analyzing-cobaltstrike-malleable-c2-profiles, differing in 67 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
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
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
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.