mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
62 lines
2.9 KiB
Markdown
62 lines
2.9 KiB
Markdown
---
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name: analyzing-cobaltstrike-malleable-c2-profiles
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description: Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract
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C2 indicators, detect evasion techniques, and generate network detection signatures.
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domain: cybersecurity
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subdomain: malware-analysis
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tags:
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- cobalt-strike
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- malleable-c2
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- c2-detection
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- beacon-analysis
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- network-signatures
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- threat-hunting
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- red-team-tools
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.AE-02
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- RS.AN-03
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- ID.RA-01
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- DE.CM-01
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---
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# Analyzing CobaltStrike Malleable C2 Profiles
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## Overview
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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.
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## When to Use
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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
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## Prerequisites
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- Python 3.9+ with `dissect.cobaltstrike` and/or `pyMalleableC2`
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- Sample Malleable C2 profiles (available from public repositories)
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- Understanding of HTTP protocol and Cobalt Strike beacon communication model
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- Network monitoring tools (Suricata/Snort) for signature deployment
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- PCAP analysis tools for traffic validation
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## Steps
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1. Install libraries: `pip install dissect.cobaltstrike` or `pip install pyMalleableC2`
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2. Parse profile with `C2Profile.from_path("profile.profile")`
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3. Extract HTTP GET/POST block configurations (URIs, headers, parameters)
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4. Identify user agent strings and spoof targets
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5. Extract sleep time, jitter percentage, and DNS beacon settings
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6. Analyze process injection settings (spawn-to, allocation technique)
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7. Generate Suricata/Snort signatures from extracted network indicators
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8. Compare profile against known threat actor profile collections
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9. Extract staging URIs and payload delivery mechanisms
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10. Produce detection report with IOCs and recommended network signatures
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## Expected Output
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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.
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