pinkpixel-dev/analyzing-cobaltstrike-malleable-c2-profiles
SKILLS/analyzing-cobaltstrike-malleable-c2-profiles/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add pinkpixel-dev/skills-collection-1 analyzing-cobaltstrike-malleable-c2-profilesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 15, 2026, 10:01 PM25.5s3 files scanned
SKILL.md
- name:
- analyzing-cobaltstrike-malleable-c2-profiles
- description:
- 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.
- domain:
- cybersecurity
- subdomain:
- malware-analysis
- tags:
- [cobalt-strike, malleable-c2, c2-detection, beacon-analysis, network-signatures, threat-hunting, red-team-tools]
- version:
- 1.0
- author:
- mahipal
- license:
- Apache-2.0
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
- 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
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.
Related Skills
pinkpixel-dev/building-threat-hunt-hypothesis-framework
development
VerifiedTrustedCommunity
Build a systematic threat hunt hypothesis framework that transforms threat intelligence, attack patterns, and environmental data into testable hunting hypotheses.
7SKILL.mdUpdated Jul 15, 2026
pinkpixel-dev/building-threat-feed-aggregation-with-misp
development
VerifiedTrustedCommunity
Deploy MISP (Malware Information Sharing Platform) to aggregate, correlate, and distribute threat intelligence feeds from multiple sources for centralized IOC management and automated SIEM integration.
7SKILL.mdUpdated Jul 15, 2026
pinkpixel-dev/building-threat-actor-profile-from-osint
development
VerifiedTrustedCommunity
Build comprehensive threat actor profiles using open-source intelligence (OSINT) techniques to document adversary motivations, capabilities, infrastructure, and TTPs for proactive defense.
7SKILL.mdUpdated Jul 15, 2026
pinkpixel-dev/building-soc-playbook-for-ransomware
development
VerifiedTrustedCommunity
Builds a structured SOC incident response playbook for ransomware attacks covering detection, containment, eradication, and recovery phases with specific SIEM queries, isolation procedures, and decision trees. Use when SOC teams need formalized response procedures for ransomware incidents aligned to NIST SP 800-61 and MITRE ATT&CK ransomware techniques.
7SKILL.mdUpdated Jul 15, 2026