Anthropic Cybersecurity Skills is a library of structured cybersecurity procedures for AI agents, covering security domains and mappings to established security frameworks. It is for authorized security analysis, penetration testing, incident response, research, defense, and education across compatible AI platforms. The catalogue entries package parts of this library as agent skills, instructions, or a plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-of-malwaregit clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware)<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Output Handling · line 97 Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.03131 |
| Opus 5 | $0.00041 | $0.01566 |
| Sonnet 5 | $0.00016 | $0.00626 |
| Haiku 4.5 | $0.00008 | $0.00313 |
Grade A, and why
analyzing-network-traffic-of-malware scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run( Copies of this mod
6 near-identical copies found in the catalogue:
- analyzing-network-traffic-of-malware — 100% identical, 0 lines differ
- analyzing-network-traffic-of-malware — 100% identical, 0 lines differ
- analyzing-network-traffic-of-malware — 94% identical, 14 lines differ
- analyzing-network-traffic-of-malware — 94% identical, 30 lines differ
- analyzing-network-traffic-of-malware — 94% identical, 30 lines differ
- analyzing-network-traffic-of-malware — 94% identical, 30 lines differ
How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Network Traffic of Malware
When to Use
- Sandbox execution has captured a PCAP file and the network behavior needs detailed analysis
- Identifying the C2 protocol structure for writing network detection signatures
- Determining what data the malware exfiltrates and to which external infrastructure
- Analyzing DNS tunneling, domain generation algorithms (DGA), or fast-flux behavior
- Creating Suricata/Snort signatures based on observed malware network patterns
Do not use for host-based analysis of malware behavior; use Cuckoo sandbox reports or Volatility memory analysis for process-level activity.
Prerequisites
- Wireshark 4.x installed for interactive PCAP analysis
- tshark (Wireshark CLI) for scripted packet extraction
- Zeek installed for automated metadata generation from PCAPs
- Suricata with ET Open/ET Pro rulesets for signature matching
- NetworkMiner for file extraction and credential detection from PCAPs
- Python 3.8+ with
scapyanddpktfor programmatic packet analysis
Workflow
Step 1: Initial PCAP Overview
Get a high-level understanding of the network traffic:
# Capture statistics
capinfos malware.pcap
# Protocol hierarchy
tshark -r malware.pcap -q -z io,phs
# Endpoint statistics (top talkers)
tshark -r malware.pcap -q -z endpoints,ip
# Conversation statistics
tshark -r malware.pcap -q -z conv,tcp
# DNS query summary
tshark -r malware.pcap -q -z dns,tree
Step 2: Analyze DNS Activity
Examine DNS queries for DGA, tunneling, or C2 domain resolution:
# Extract all DNS queries
tshark -r malware.pcap -T fields -e frame.time -e dns.qry.name -e dns.a \
-Y "dns.flags.response == 1" | sort
# Detect DGA patterns (high entropy domain names)
python3 << 'PYEOF'
import math
from collections import Counter
def entropy(s):
p = [n/len(s) for n in Counter(s).values()]
return -sum(pi * math.log2(pi) for pi in p if pi > 0)
# Parse DNS queries from tshark output
import subprocess
result = subprocess.run(
["tshark", "-r", "malware.pcap", "-T", "fields", "-e", "dns.qry.name",
"-Y", "dns.flags.response == 0"],
capture_output=True, text=True
)
domains = set(result.stdout.strip().split('\n'))
print("Suspicious DNS queries (high entropy):")
for domain in domains:
if domain:
subdomain = domain.split('.')[0]
ent = entropy(subdomain)
if ent > 3.5 and len(subdomain) > 10:
print(f" {domain} (entropy: {ent:.2f})")
PYEOF
# Detect DNS tunneling (large TXT responses)
tshark -r malware.pcap -T fields -e dns.qry.name -e dns.txt \
-Y "dns.resp.type == 16 and dns.resp.len > 100"
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 341 lines · 81 tokens per session scan A 83c6be312c1b
analyzing-network-traffic-of-malware is a skill published in the GitHub repository mukul975/Anthropic-Cybersecurity-Skills (32,631 stars, last pushed 12d ago), licensed Apache-2.0. It adds 81 tokens to every session and 3,131 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
analyzing-network-traffic-of-malware
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding…
analyzing-network-traffic-of-malware
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding…
analyzing-network-traffic-of-malware
A guide to examining network traffic produced by malware, using packet captures and tools such as Wireshark, Zeek, and Suricata. A packet capture is a recorded copy of network communications.
analyzing-network-traffic-of-malware
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding…
analyzing-network-traffic-of-malware
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding…
analyzing-network-traffic-of-malware
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding…