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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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Based on SOC occupation classification
Produces a structured Windows system report that synthesises performance and security signals into a single Markdown document and writes it to disk. Combines sysinfo, tasklist, network connections, recent event log entries, Defender state, autoruns, hotfix list, and disk usage into an executive summary plus per-axis sections plus actionable findings. Activates when the user asks Lucy to "generate / produce / build / write / compile / elaborate" a "report / informe / reporte / auditoría / overview" of "the machine / la maquina / mi equipo / mi laptop / the host" with optional persistence target (escritorio / desktop / archivo / .md / .pdf).
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
| 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 |
| nist_csf | ["DE.AE-02","RS.AN-03","ID.RA-01","DE.CM-01"] |
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.
dissect.cobaltstrike and/or pyMalleableC2pip install dissect.cobaltstrike or pip install pyMalleableC2C2Profile.from_path("profile.profile")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.