What it does. 'Use YARA pattern-matching rules to hunt for malware, suspicious files, Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-threat-hunting-with-yara-rules, or copy the skill folder into ~/.claude/skills/performing-threat-hunting-with-yara-rules/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/performing-threat-hunting-with-yara-rules/SKILL.md

SKILL.md (verbatim)

name: performing-threat-hunting-with-yara-rules
description: 'Use YARA pattern-matching rules to hunt for malware, suspicious files,
  and indicators of compromise across filesystems and memory dumps. Covers rule authoring,
  yara-python scanning, and integration with threat intel feeds.

  '
domain: cybersecurity
subdomain: threat-hunting
tags:
- yara
- malware-detection
- threat-hunting
- pattern-matching
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- Executable Denylisting
- Execution Isolation
- File Metadata Consistency Validation
- Content Format Conversion
- File Content Analysis
nist_csf:
- DE.CM-01
- DE.AE-02
- DE.AE-07
- ID.RA-05
mitre_attack:
- T1046
- T1057
- T1082
- T1083
- T1005

Performing Threat Hunting with YARA Rules

Scan files, directories, and memory dumps using YARA rules to identify malware families, suspicious patterns, and IOC matches.

When to Use

• Proactively hunting for unknown malware variants across network shares, endpoints, and email attachments
• Scanning quarantine directories or sandbox outputs for malware family classification
• Searching process memory dumps for injected code or in-memory-only payloads
• Validating threat intelligence IOCs against a large corpus of collected samples
• Triaging incident response artifacts to identify known malware families quickly
• Building automated detection pipelines that scan new files on ingestion

Do not use for real-time endpoint protection (use EDR agents instead); YARA scanning is best suited for batch hunting, triage, and post-collection analysis where scan latency is acceptable.

Prerequisites

• YARA 4.x installed (apt install yara on Debian/Ubuntu, brew install yara on macOS)
• Python 3.8+ with yara-python (pip install yara-python)
• yarGen for automated rule generation (git clone https://github.com/Neo23x0/yarGen)
• Sample malware corpus or suspicious files for scanning (from malware zoos, VT, or incident artifacts)
• Optional: pefile for PE header analysis, malduck for memory carving
• Threat intel YARA rule sets (e.g., YARA-Rules community repository, Florian Roth signature-base)

Workflow

Step 1: Install YARA and Python Bindings

# Linux
sudo apt update && sudo apt install -y yara

# Python bindings
pip install yara-python

# Verify installation
yara --version
python3 -c "import yara; print(yara.YARA_VERSION)"

Step 2: Write a Basic YARA Rule

Create rules that match on strings, hex patterns, and file metadata:

// File: rules/emotet_loader.yar
rule Emotet_Loader_2026 {
    meta:
        author = "Threat Intel Team"
        description = "Detects Emotet first-stage loader DLL"
        date = "2026-01-20"
        reference = "https://attack.mitre.org/software/S0367/"
        mitre_attack = "T1059.001, T1055.001"
        severity = "critical"

    strings:
        // Emotet export function name patterns
        $export1 = "DllRegisterServer" ascii
        $export2 = "RunDLL" ascii nocase

        // Obfuscated string decryption routine
        $decrypt_loop = { 8B 45 ?? 33 45 ?? 89 45 ?? 8B 4D ?? 03 4D ?? }

        // PowerShell download cradle in embedded script
        $ps_cradle = /powershell[^\n]{0,50}-e(nc|ncodedcommand)/i

        // Known C2 URI patterns
        $uri1 = "/wp-content/uploads/" ascii
        $uri2 = "/wp-admin/css/" ascii
        $uri3 = "/wp-includes/" ascii

        // PE characteristics
        $mz = "MZ" at 0

    condition:
        $mz and
        filesize < 2MB and
        (
            ($export1 and $decrypt_loop) or
            ($ps_cradle and any of ($uri*)) or
            (2 of ($uri*) and $decrypt_loop)
        )
}

Step 3: Write Advanced Rules with Modules

Use YARA modules for PE header inspection and math-based entropy checks:

import "pe"
import "math"

rule Suspicious_Packed_Executable {
    meta:
        author = "Threat Hunting Team"
        description = "Detects PE files with high entropy sections indicating packing or encryption"
        severity = "medium"

    condition:
        pe.is_pe and
        pe.number_of_sections > 0 and
        for any section in pe.sections : (
            math.entropy(section.offset, section.size) > 7.2 and
            section.size > 1024
        ) and
        pe.imports("kernel32.dll", "VirtualAlloc") and
        pe.imports("kernel32.dll", "VirtualProtect")
}

rule Suspicious_UPX_Modified {
    meta:
        description = "Detects UPX-packed binaries with tampered section names"
        severity = "medium"

    strings:
        $upx_magic = { 55 50 58 21 }  // UPX!

    condition:
        pe.is_pe and
        $upx_magic and
        not (
            pe.sections[0].name == "UPX0" and
            pe.sections[1].name == "UPX1"
        )
}

Step 4: Scan Files and Directories with yara-python

import yara
import os
import json
from datetime import datetime
from pathlib import Path

def compile_rules(rule_paths):
    """Compile YARA rules from one or more .yar files."""
    rule_files = {}
    for i, path in enumerate(rule_paths):
        namespace = Path(path).stem
        rule_files[namespace] = path
    return yara.compile(filepaths=rule_files)

def scan_directory(rules, target_dir, recursive=True):
    """Scan a directory for matches and return structured results."""
    results = []
    scan_count = 0
    error_count = 0

    for root, dirs, files in os.walk(target_dir):
        for filename in files:
            filepath = os.path.join(root, filename)
            scan_count += 1
            try:
                matches = rules.match(filepath, timeout=60)
                if matches:
                    for match in matches:
                        result = {
                            "file": filepath,
                            "rule": match.rule,
                            "namespace": match.namespace,
                            "tags": match.tags,
                            "meta": match.meta,
                            "strings": [],
                            "scan_time": datetime.utcnow().isoformat()
                        }
                        for offset, identifier, data in match.strings:
                            result["strings"].append({
                                "offset": hex(offset),
                                "identifier": identifier,
                                "data": data.hex() if isinstance(data, bytes) else data
                            })
                        results.append(result)
                        print(f"  MATCH: {match.rule} -> {filepath}")
            except yara.TimeoutError:
                error_count += 1
                print(f"  TIMEOUT scanning {filepath}")
            except yara.Error as e:
                error_count += 1

        if not recursive:
            break

    print(f"\nScan complete: {scan_count} files scanned, "
          f"{len(results)} matches, {error_count} errors")
    return results

# Compile and scan
rules = compile_rules([
    "rules/emotet_loader.yar",
    "rules/suspicious_packed.yar"
])

matches = scan_directory(rules, "/mnt/evidence/collected_samples/")

# Export results
with open("yara_scan_results.json", "w") as f:
    json.dump(matches, f, indent=2)

Step 5: Scan Process Memory Dumps

Hunt for in-memory indicators that only exist in running processes:

import yara

def scan_memory_dump(rules, dump_path):
    """Scan a process memory dump for YARA matches."""
    matches = rules.match(dump_path, timeout=120)

    for match in matches:
        print(f"Rule: {match.rule}")
        print(f"  Severity: {match.meta.get('severity', 'unknown')}")
        for offset, identifier, data in match.strings:
            # Show context around the match
            print(f"  String {identifier} at offset {hex(offset)}")
            if len(data) <= 64:
                print(f"    Data: {data.hex()}")

    return matches

# Rules targeting in-memory artifacts
memory_rules = yara.compile(source="""
rule Cobalt_Strike_Beacon_Memory {
    meta:
        description = "Detects Cobalt Strike beacon in process memory"
        severity = "critical"
    strings:
        $config_start = { 2E 2F 2E 2F 2E 2C }
        $sleep_mask = { 48 8B 44 24 ?? 48 89 44 24 ?? 48 8B 44 24 }
        $named_pipe = "\\\\\\\\.\\\\pipe\\\\msagent_" ascii
        $watermark = { 00 00 00 00 00 00 ?? ?? 00 00 }
    condition:
        2 of them
}
""")

scan_memory_dump(memory_rules, "/mnt/evidence/lsass_dump.dmp")

Step 6: Generate Rules Automatically with yarGen

Use yarGen to create rules from malware samples by extracting unique strings:

# Clone and set up yarGen
git clone https://github.com/Neo23x0/yarGen.git
cd yarGen
pip install -r requirements.txt

# Download the string databases (run once)
python3 yarGen.py --update

# Generate rules from a directory of malware samples
python3 yarGen.py \
    -m /mnt/evidence/malware_samples/ \
    -o generated_rules.yar \
    --excludegood \
    -p "AutoGen" \
    -a "Threat Hunting Team" \
    --score 50

# Generate rules for a single sample with maximum detail
python3 yarGen.py \
    -m /mnt/evidence/malware_samples/suspicious.exe \
    -o single_sample_rule.yar \
    --opcodes \
    --debug

Step 7: Integrate Community Rule Sets

Download and combine rules from public threat intelligence repositories:

# Clone Florian Roth's signature-base (large community rule set)
git clone https://github.com/Neo23x0/signature-base.git

# Clone YARA-Rules community repository
git clone https://github.com/Yara-Rules/rules.git yara-community-rules

# Clone ReversingLabs YARA rules
git clone https://github.com/reversinglabs/reversinglabs-yara-rules.git

import yara
from pathlib import Path

def load_rule_directory(rule_dir, extensions=(".yar", ".yara")):
    """Load all YARA rules from a directory tree."""
    rule_files = {}
    for ext in extensions:
        for rule_file in Path(rule_dir).rglob(f"*{ext}"):
            namespace = rule_file.stem
            # Avoid namespace collisions
            if namespace in rule_files:
                namespace = f"{rule_file.parent.name}_{namespace}"
            rule_files[namespace] = str(rule_file)

    print(f"Loading {len(rule_files)} rule files from {rule_dir}")
    try:
        compiled = yara.compile(filepaths=rule_files)
        return compiled
    except yara.SyntaxError as e:
        print(f"Syntax error in rules: {e}")
        # Fall back to loading rules one by one, skipping broken ones
        valid_rules = {}
        for ns, path in rule_files.items():
            try:
                yara.compile(filepath=path)
                valid_rules[ns] = path
            except yara.SyntaxError:
                print(f"  Skipping broken rule: {path}")
        return yara.compile(filepaths=valid_rules)

# Load and scan with community rules
community_rules = load_rule_directory("signature-base/yara/")
matches = community_rules.match("/mnt/evidence/suspicious_file.exe", timeout=120)

for m in matches:
    print(f"Matched: {m.rule} (namespace: {m.namespace})")

Step 8: Build a Continuous Hunting Pipeline

Automate scanning of new files as they arrive using filesystem monitoring:

import yara
import time
import json
import hashlib
from pathlib import Path
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler

class YaraHuntingHandler(FileSystemEventHandler):
    def __init__(self, rules, alert_file="yara_alerts.jsonl"):
        self.rules = rules
        self.alert_file = alert_file
        self.scanned_hashes = set()

    def on_created(self, event):
        if event.is_directory:
            return
        self._scan_file(event.src_path)

    def _scan_file(self, filepath):
        # Deduplicate by file hash
        try:
            file_hash = hashlib.sha256(Path(filepath).read_bytes()).hexdigest()
        except (PermissionError, FileNotFoundError):
            return

        if file_hash in self.scanned_hashes:
            return
        self.scanned_hashes.add(file_hash)

        matches = self.rules.match(filepath, timeout=60)
        if matches:
            alert = {
                "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
                "file": filepath,
                "sha256": file_hash,
                "matches": [
                    {"rule": m.rule, "severity": m.meta.get("severity", "unknown")}
                    for m in matches
                ]
            }
            with open(self.alert_file, "a") as f:
                f.write(json.dumps(alert) + "\n")
            print(f"ALERT: {filepath} matched {len(matches)} rules")

# Set up continuous monitoring
rules = yara.compile(filepaths={"hunting": "rules/all_hunting_rules.yar"})
handler = YaraHuntingHandler(rules)
observer = Observer()
observer.schedule(handler, path="/mnt/quarantine/", recursive=True)
observer.start()
print("YARA hunting pipeline active. Monitoring /mnt/quarantine/ ...")

Verification

• Compile all custom rules without syntax errors: yara -w rules/*.yar /dev/null
• Confirm rules match known-good malware samples from your test corpus (true positive validation)
• Verify rules do NOT match a goodware corpus of common system files (false positive testing)
• Test scanning performance: single file scan should complete within timeout threshold
• Validate yarGen output rules compile and produce meaningful matches against the input samples
• Check that community rule sets load without critical syntax errors after filtering
• Confirm the continuous hunting pipeline generates alerts in JSONL format when test files are dropped
• Cross-reference YARA matches against VirusTotal or sandbox results to validate detection accuracy

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: Threat Hunting with YARA Rules

yara-python Library

Installation

pip install yara-python

Compile and Scan

import yara

# Compile from source string
rules = yara.compile(source='rule test { strings: $a = "malware" condition: $a }')

# Compile from file
rules = yara.compile(filepath='/path/to/rules.yar')

# Compile from directory (multiple files)
rules = yara.compile(filepaths={'ns1': '/rules/rule1.yar', 'ns2': '/rules/rule2.yar'})

# Scan file
matches = rules.match('/path/to/suspect.exe')
for m in matches:
    print(m.rule, m.meta, m.strings, m.tags)

# Scan data (bytes)
matches = rules.match(data=open('/path/to/file', 'rb').read())

# Scan with timeout (seconds)
matches = rules.match('/path/to/file', timeout=60)

YARA CLI

# Scan file with single rule
yara rule.yar suspect.exe

# Scan directory recursively
yara -r rules.yar /path/to/directory/

# Show matching strings
yara -s rule.yar suspect.exe

# Show metadata
yara -e rule.yar suspect.exe

# Compile rules to binary
yarac rules.yar compiled.yarc
yara compiled.yarc suspect.exe

# Scan with tag filter
yara -t malware rules.yar /path/

YARA Rule Structure

rule Example_Rule {
    meta:
        author = "analyst"
        description = "Detects example pattern"
        severity = "high"
        reference = "https://example.com"
    strings:
        $text = "suspicious_string" ascii nocase
        $hex = { 4D 5A 90 00 }
        $regex = /eval\(base64_decode/
    condition:
        uint16(0) == 0x5A4D and 2 of ($text, $hex, $regex)
}

Match Object Fields

Community Rule Sources

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