--- name: performing-malware-triage-with-yara-7d3c9093fa89 description: 'Performs rapid malware triage and classification using YARA rules that match file patterns, strings, byte sequences, and structural characteristics against known malware families and suspicious indicators, covering rule writing, scanning, and integration into analysis pipelines. Use when classifying a batch of malware samples against known family signatures, writing detection rules for a newly analyzed malware family, or performing signature-based malware triage. ' domain: cybersecurity subdomain: malware-analysis tags: - malware - YARA - triage - classification - pattern-matching version: 1.0.0 author: mahipal license: Apache-2.0 nist_csf: - DE.AE-02 - RS.AN-03 - ID.RA-01 - DE.CM-01 mitre_attack: - T1027 - T1055 - T1140 - T1497 - T0816 --- # Performing Malware Triage with YARA ## When to Use - Rapidly classifying a large batch of malware samples against known family signatures - Writing detection rules for a newly analyzed malware family based on unique byte patterns - Scanning file shares, endpoints, or memory dumps for indicators of a specific threat - Building automated triage pipelines that classify samples before manual analysis - Hunting for variants of a known threat across an enterprise using YARA scans **Do not use** as the sole analysis method; YARA triage identifies known patterns but does not reveal new or unknown malware behaviors. ## Prerequisites - YARA 4.x installed (`apt install yara` or `pip install yara-python`) - YARA rule repositories (YARA-Rules, awesome-yara, Malpedia rules, Florian Roth's signature-base) - Python 3.8+ with `yara-python` for scripted scanning - Sample collection organized in a directory structure for batch scanning - Understanding of PE file format, hex patterns, and regular expressions for rule writing ## Workflow ### Step 1: Scan Samples with Existing Rule Sets Apply community and commercial YARA rules to classify samples: ```bash # Scan a single file yara -s malware_rules.yar suspect.exe # Scan a directory of samples yara -r malware_rules.yar /path/to/samples/ # Scan with multiple rule files yara -r rules/apt_rules.yar rules/ransomware_rules.yar rules/trojan_rules.yar suspect.exe # Scan with timeout (prevent hanging on large files) yara -t 30 malware_rules.yar suspect.exe # Scan and show matching strings yara -s -r malware_rules.yar suspect.exe # Scan with compiled rules (faster for repeated scans) yarac malware_rules.yar compiled_rules.yarc yara compiled_rules.yarc suspect.exe ``` ```bash # Download community rule sets git clone https://github.com/Yara-Rules/rules.git yara-community-rules git clone https://github.com/Neo23x0/signature-base.git signature-base # Scan with signature-base yara -r signature-base/yara/*.yar suspect.exe ``` ### Step 2: Write Rules for Unique String Patterns Create YARA rules based on strings extracted during malware analysis: ``` rule MalwareX_Strings { meta: description = "Detects MalwareX based on unique strings" author = "analyst" date = "2025-09-15" reference = "Internal Analysis Report #1547" hash = "e3b0c44298fc1c149afbf4c8996fb924" tlp = "WHITE" strings: // C2 URL pattern $url1 = "/gate.php?id=" ascii $url2 = "/panel/connect.php" ascii // Unique mutex name $mutex = "Global\\CryptLocker_2025" ascii wide // User-Agent string $ua = "Mozilla/5.0 (compatible; MSIE 10.0)" ascii // Registry persistence path $reg = "Software\\Microsoft\\Windows\\CurrentVersion\\Run\\WindowsUpdate" ascii // Campaign identifier $campaign = "campaign_2025_q3" ascii condition: uint16(0) == 0x5A4D and // PE file (MZ header) filesize < 500KB and // Size constraint ($url1 or $url2) and // At least one C2 URL ($mutex or $campaign) and // Campaign identifier $ua // Specific User-Agent } ``` ### Step 3: Write Rules for Byte Patterns Create rules matching specific code sequences: ``` rule MalwareX_Decryptor { meta: description = "Detects MalwareX XOR decryption routine" author = "analyst" date = "2025-09-15" strings: // XOR decryption loop (x86 assembly) // mov al, [esi+ecx] // xor al, [edi+ecx] // mov [esi+ecx], al // inc ecx // cmp ecx, edx // jl loop $xor_loop = { 8A 04 0E 32 04 0F 88 04 0E 41 3B CA 7C F3 } // RC4 KSA initialization (256-byte loop) $rc4_ksa = { 33 C0 88 04 ?8 40 3D 00 01 00 00 7? } // Embedded RSA public key marker $rsa_key = { 06 02 00 00 00 A4 00 00 52 53 41 31 } // PUBLICKEYBLOB condition: uint16(0) == 0x5A4D and ($xor_loop or $rc4_ksa) and $rsa_key } ``` ### Step 4: Write Rules with PE Module Leverage YARA's PE module for structural detection: ``` import "pe" import "hash" import "math" rule MalwareX_PE_Characteristics { meta: description = "Detects MalwareX by PE structure and imports" author = "analyst" condition: pe.is_pe and // Compiled within specific timeframe pe.timestamp > 1693526400 and // After 2023-09-01 pe.timestamp < 1727740800 and // Before 2024-10-01 // Specific import hash pe.imphash() == "a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6" or // Suspicious import combination ( pe.imports("kernel32.dll", "VirtualAllocEx") and pe.imports("kernel32.dll", "WriteProcessMemory") and pe.imports("kernel32.dll", "CreateRemoteThread") and pe.imports("wininet.dll", "InternetOpenA") ) or // High entropy .text section (packed) ( for any section in pe.sections : ( section.name == ".text" and math.entropy(section.raw_data_offset, section.raw_data_size) > 7.0 ) ) } rule MalwareX_Rich_Header { meta: description = "Detects MalwareX by Rich header hash" condition: pe.is_pe and hash.md5(pe.rich_signature.clear_data) == "abc123def456abc123def456abc123de" } ``` ### Step 5: Batch Triage with Python Automate scanning of sample collections: ```python import yara import os import json import hashlib from datetime import datetime # Compile all rule files rule_files = { "apt": "rules/apt_rules.yar", "ransomware": "rules/ransomware_rules.yar", "trojan": "rules/trojan_rules.yar", "custom": "rules/custom_rules.yar", } rules = yara.compile(filepaths=rule_files) # Scan sample directory results = [] sample_dir = "/path/to/samples" for filename in os.listdir(sample_dir): filepath = os.path.join(sample_dir, filename) if not os.path.isfile(filepath): continue with open(filepath, "rb") as f: data = f.read() sha256 = hashlib.sha256(data).hexdigest() matches = rules.match(filepath) result = { "filename": filename, "sha256": sha256, "size": len(data), "matches": [], "classification": "UNKNOWN", } for match in matches: result["matches"].append({ "rule": match.rule, "namespace": match.namespace, "tags": match.tags, "strings": [(hex(s[0]), s[1], s[2].decode("utf-8", errors="replace")[:100]) for s in match.strings] if match.strings else [] }) if result["matches"]: result["classification"] = result["matches"][0]["namespace"].upper() results.append(result) # Summary classified = sum(1 for r in results if r["classification"] != "UNKNOWN") print(f"Scanned: {len(results)} samples") print(f"Classified: {classified} ({classified/len(results)*100:.1f}%)") print(f"Unknown: {len(results)-classified}") # Export results with open("triage_results.json", "w") as f: json.dump(results, f, indent=2) ``` ### Step 6: Validate and Optimize Rules Test rules for false positives and performance: ```bash # Test rule syntax yara -C custom_rules.yar # Scan known-clean directory to check false positives yara -r custom_rules.yar /path/to/clean_files/ > false_positives.txt wc -l false_positives.txt # Benchmark rule performance time yara -r custom_rules.yar /path/to/large_sample_collection/ # Profile individual rule performance yara -p custom_rules.yar suspect.exe ``` ## Key Concepts | Term | Definition | |------|------------| | **YARA Rule** | Pattern matching rule defining strings, byte sequences, and conditions that identify a specific file or malware family | | **Condition** | Boolean expression combining string matches, file properties, and module functions to determine if a rule matches | | **Hex String** | Byte pattern with optional wildcards (??) and jumps ([N-M]) for matching machine code or binary data | | **PE Module** | YARA module providing access to PE file properties (imports, sections, timestamps, resources) for structural matching | | **Imphash** | MD5 hash of a PE file's import table; samples from the same family often share import hashes | | **Rich Header** | Undocumented PE structure containing compiler/linker metadata; consistent within malware build environments | | **YARA-C** | Compiled YARA rule format enabling faster scanning by pre-compiling rules for repeated use | ## Tools & Systems - **YARA**: Pattern matching engine for identifying and classifying malware based on text, hex, and structural patterns - **yara-python**: Python bindings for YARA enabling scripted scanning, rule compilation, and integration with analysis pipelines - **yarGen**: Automatic YARA rule generator that creates rules from malware samples by identifying unique strings and opcodes - **YARA-Rules (GitHub)**: Community-maintained repository of YARA rules covering malware families, exploits, and suspicious indicators - **Malpedia YARA**: Curated YARA rules from the Malpedia malware encyclopedia with high-quality family-specific rules ## Common Scenarios ### Scenario: Creating Detection Rules for a New Malware Family **Context**: Reverse engineering of a new malware sample has identified unique strings, byte patterns, and PE characteristics. YARA rules are needed for enterprise-wide hunting and ongoing detection. **Approach**: 1. Extract unique strings from the unpacked binary (C2 URLs, mutex names, registry paths) 2. Identify unique byte sequences from the encryption routine or C2 protocol (from Ghidra analysis) 3. Record PE characteristics (imphash, Rich header hash, section names, compilation timestamp range) 4. Write a YARA rule combining string, byte pattern, and PE module conditions 5. Test against the known malware samples to confirm true positive detection 6. Test against a clean file corpus (Windows system files, common applications) to verify zero false positives 7. Deploy to enterprise scanning infrastructure and threat intelligence platform **Pitfalls**: - Writing rules too specific to a single sample (will not detect variants with minor changes) - Writing rules too generic (matching legitimate software, causing false positives) - Using strings that appear in common libraries or frameworks (e.g., OpenSSL strings) - Not testing on a sufficiently large clean corpus before deployment ## Output Format ``` YARA TRIAGE RESULTS ===================== Scan Date: 2025-09-15 Rule Sets: apt_rules (847 rules), ransomware_rules (312 rules), trojan_rules (1,204 rules), custom_rules (45 rules) Samples Scanned: 2,500 Processing Time: 47 seconds CLASSIFICATION SUMMARY APT: 12 samples (0.5%) Ransomware: 187 samples (7.5%) Trojan: 423 samples (16.9%) Unknown: 1,878 samples (75.1%) TOP MATCHING RULES Rule Matches Family MalwareX_C2_Beacon 45 MalwareX LockBit3_Ransom_Note 38 LockBit 3.0 Emotet_Epoch5_Loader 32 Emotet CobaltStrike_Beacon_Config 28 Cobalt Strike QakBot_DLL_Loader 25 QakBot SAMPLE DETAIL File: suspect.exe SHA-256: e3b0c44298fc1c149afbf4c8996fb924... Matches: [1] MalwareX_Strings (custom) - $url1 at 0x4A20: "/gate.php?id=" - $mutex at 0x5100: "Global\\CryptLocker_2025" [2] MalwareX_Decryptor (custom) - $xor_loop at 0x401200: { 8A 04 0E 32 04 0F ... } [3] MalwareX_PE_Characteristics (custom) - PE import combination matched Classification: MALWAREX (HIGH CONFIDENCE) ``` --- Source attribution: costrict-plugins-repo Original project: https://github.com/costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills/blob/1b344d155f00bae5cf27d8eba9508e8f5050d904/skills/performing-malware-triage-with-yara/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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