mirror of https://github.com/SHOGGOTH-SECTOR/sica-fondt.git synced 2026-08-01 08:30:20 +00:00

Consolidate 22 sibling repos into layered organism structure

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

2026-06-10 06:53:01 +00:00

2.9 KiB

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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.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

Install libraries: pip install dissect.cobaltstrike or pip 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.

2.9 KiB Raw Blame History