analyzing-indicators-of-compromise

Q: How do I install analyzing-indicators-of-compromise?

Run npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-indicators-of-compromise in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-indicators-of-compromise/ (personal) or .claude/skills/analyzing-indicators-of-compromise/ (project).

Q: Which AI agents can use analyzing-indicators-of-compromise?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Q: Is analyzing-indicators-of-compromise safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Q: Who maintains analyzing-indicators-of-compromise?

It is published by mukul975 in mukul975/anthropic-cybersecurity-skills. It has 467 recorded installs.

by mukul975Security

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

Installs: 467
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-indicators-of-compromise

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads analyzing-indicators-of-compromise automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Analyzing Indicators of Compromise

When to Use

Use this skill when:

A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage
Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls
An incident investigation requires contextual enrichment of observed network artifacts

Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).

Prerequisites

VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup
AbuseIPDB API key for IP reputation checks
MISP instance or TIP for cross-referencing against known campaigns
Python with requests and vt-py libraries, or SOAR platform with pre-built connectors

Workflow

Step 1: Normalize and Classify IOC Types

Before enriching, classify each IOC:

IPv4/IPv6 address: Check if RFC 1918 private (skip external enrichment), validate format
Domain/FQDN: Defang for safe handling (evil[.]com), extract registered domain via tldextract
URL: Extract domain + path separately; check for redirectors
File hash: Identify hash type (MD5/SHA-1/SHA-256); prefer SHA-256 for uniqueness
Email address: Split into domain (check MX/DMARC) and local part for pattern analysis

Defang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.

Step 2: Multi-Source Enrichment

VirusTotal (file hash, URL, IP, domain):

import vt

client = vt.Client("YOUR_VT_API_KEY")

# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")

# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()

AbuseIPDB (IP addresses):

import requests

response = requests.get(
    "https://api.abuseipdb.com/api/v2/check",
    headers={"Key": "YOUR_KEY", "Accept": "application/json"},
    params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")

MalwareBazaar (file hashes):

response = requests.post(
    "https://mb-api.abuse.ch/api/v1/",
    data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
    print(result["data"][0]["tags"], result["data"][0]["signature"])

Step 3: Contextualize with Campaign Attribution

Query MISP for existing events matching the IOC:

from pymisp import PyMISP

misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
    print(event["Event"]["info"], event["Event"]["threat_level_id"])

Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).

Step 4: Assign Confidence Score and Disposition

Apply a tiered decision framework:

Block (High Confidence ≥ 70%): ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign
Monitor/Alert (Medium 40–69%): 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution
Whitelist/Investigate (Low <40%): ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)
False Positive: Legitimate business service incorrectly flagged; document and exclude from future alerts

Step 5: Document and Distribute

Record findings in TIP/MISP with:

All enrichment data collected (timestamps, source, score)
Disposition decision and rationale
Blocking actions taken (firewall, proxy, DNS sinkhole)
Related incident ticket number

Export to STIX indicator object with confidence field set appropriately.

Key Concepts

Term	Definition
IOC	Indicator of Compromise — observable network or host artifact indicating potential compromise
Enrichment	Process of adding contextual data to a raw IOC from multiple intelligence sources
Defanging	Modifying IOCs (replacing `.` with `[.]`) to prevent accidental activation in documentation
False Positive Rate	Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds
Sinkhole	DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely
TTL	Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days

Tools & Systems

VirusTotal: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments
AbuseIPDB: Community-maintained IP reputation database with 90-day abuse report history
MalwareBazaar (abuse.ch): Free malware hash repository with YARA rule associations and malware family tagging
URLScan.io: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage
Shodan: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment

Common Pitfalls

Blocking shared infrastructure: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.
VT score obsession: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.
Missing defanging: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.
No expiration policy: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.
Over-relying on single source: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.

FAQ

What does the analyzing-indicators-of-compromise skill do?

How do I install analyzing-indicators-of-compromise?

Run npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-indicators-of-compromise in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-indicators-of-compromise/ (personal) or .claude/skills/analyzing-indicators-of-compromise/ (project).

Which AI agents can use analyzing-indicators-of-compromise?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is analyzing-indicators-of-compromise safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains analyzing-indicators-of-compromise?

It is published by mukul975 in mukul975/anthropic-cybersecurity-skills. It has 467 recorded installs.