analyzing-cobalt-strike-malleable-profiles — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited analyzing-cobalt-strike-malleable-profiles (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Parse malleable C2 profiles to extract IOCs and detection opportunities using the pyMalleableC2 library. Combine with JARM fingerprinting to identify C2 servers.
from malleablec2 import Profile
# Parse a malleable profile from file
profile = Profile.from_file("amazon.profile")
# Extract global options (sleep, jitter, user-agent)
print(profile.ast.pretty())
# Access HTTP-GET block URIs and headers for network signatures
# Access HTTP-POST block for data exfiltration patterns
# Generate JARM fingerprints for known C2 infrastructureKey analysis steps:
# Parse profile and extract detection indicators
from malleablec2 import Profile
p = Profile.from_file("cobaltstrike.profile")
print(p) # Reconstructed source
# JARM scan a suspect C2 server
import subprocess
result = subprocess.run(
["python3", "jarm.py", "suspect-server.com"],
capture_output=True, text=True
)
print(result.stdout)
# Compare fingerprint against known CS JARM hashes~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.