analyzing-cobaltstrike-malleable-c2-profiles

analyzing-cobaltstrike-malleable-c2-profiles is a skill for Claude Code from oyi77/1ai-skills. It costs 57 tokens per session (1,131 once invoked), scanned A, a copy of analyzing-cobaltstrike-malleable-c2-profiles, MIT.

A procedure for reading Cobalt Strike Malleable C2 profiles, configuration files that control how the tool’s Beacon communicates with its server. It extracts communication patterns and behaviours that may reveal how the traffic is disguised.

In plain words
What is it for?
Use it during incident response and threat hunting to extract URLs, headers, request patterns, DNS settings, and process-injection details for network detections.
Why use it?
Attackers can shape command-and-control traffic to resemble normal services, making it harder to spot. Examining the profile exposes indicators that defenders can use for investigation and detection.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 1ai-skills plugin — 209 skills, 4 commands shipped together

Good fit Use it during incident response and threat hunting to extract URLs, headers, request patterns, DNS settings, and process-injection details for network detections.

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Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/analyzing-cobaltstrike-malleable-c2-profiles
Install

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.

Any agent
npx skills add oyi77/1ai-skills --skill analyzing-cobaltstrike-malleable-c2-profiles
Clone the repo
git clone --depth 1 https://github.com/oyi77/1ai-skills

Made for: Claude Code.

Or install 1ai-skills, the plugin that ships this one along with the rest of its 209 skills, 4 commands.

Wrote this? Show the measurements

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README.md
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<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>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,131 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 102c98852a30, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

Origin

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.

cybersecurity/_deprecated/analyzing-cobaltstrike-malleable-c2-profiles/SKILL.md · 118 lines

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"

  • "Parse and analyze Cobalt Strike Malleable C2 profiles using dissect"

  • When investigating security incidents that require analyzing cobaltstrike malleable c2 profiles

  • When building detection rules or threat hunting queries for this domain

  • When SOC analysts need structured procedures for this analysis type

  • 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.cobaltstrike and/or pyMalleableC2
  • 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()}

Read the full file on GitHub · 118 lines

Changes

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.

  1. 8d ago First seen · 118 lines · 57 tokens per session scan A 102c98852a30

Subscribe to this mod's changes

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.

Related

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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…

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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.

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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.

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