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Production hardening: security fixes, code quality, 724 skills complete

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- Fix 25 shell=True subprocess calls with list-based commands
- Fix 49 verify=False in defensive skills (env-var override)
- Add timeout to 231 HTTP/subprocess/socket calls
- Fix 6 SQL injection patterns with whitelist validation
- Replace 8 __import__() with standard imports
- Remove 701 unused imports across 442 files
- Add authorized-testing disclaimers to all offensive skills
- Complete 11 incomplete skill directories
- Expand 10 stub SKILL.md files with full content
- Fix 2 YAML parse errors in frontmatter
- Fix 5 pre-existing syntax errors
- Convert 22 hardcoded paths/ports to environment variables
- Back up 21 redundant skill pairs to .bak
- Fix 2 global declaration errors
- 724/724 skills with full folder anatomy (SKILL.md + agent.py + api-reference.md + LICENSE)
- 0 compile errors across all 724 agent.py files
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skills/detecting-ransomware-encryption-behavior/SKILL.md
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---
name: detecting-ransomware-encryption-behavior
description: >
Detects ransomware encryption activity in real time using entropy analysis,
file system I/O monitoring, and behavioral heuristics. Identifies mass file
modification patterns, abnormal entropy spikes in written data, and suspicious
process behavior characteristic of ransomware encryption routines. Activates
for requests involving ransomware behavioral detection, entropy-based file
monitoring, I/O anomaly detection, or real-time encryption activity alerting.
domain: cybersecurity
subdomain: ransomware-defense
tags: [ransomware, detection, entropy, behavioral-analysis, file-monitoring, heuristics]
version: 1.0.0
author: mahipal
license: Apache-2.0
---
# Detecting Ransomware Encryption Behavior
## When to Use
- Building or tuning a behavioral detection layer for ransomware that catches unknown/zero-day variants
- Monitoring file servers and endpoints for mass encryption activity that evades signature-based detection
- Implementing entropy-based detection to identify when files are being replaced with encrypted (high-entropy) content
- Analyzing suspicious process behavior patterns: rapid sequential file opens, writes, renames, and deletes
- Validating EDR detection rules against actual ransomware encryption patterns during red team exercises
**Do not use** entropy analysis alone as the only detection signal. Compressed files (ZIP, JPEG, MP4) naturally have high entropy and will cause false positives. Always combine entropy with behavioral signals like I/O rate and file rename patterns.
## Prerequisites
- Python 3.8+ with `watchdog` and `psutil` libraries
- Administrative access for process monitoring and file system event capture
- Understanding of Shannon entropy and its application to file content analysis
- Windows: Sysmon installed for detailed process and file system event logging
- Linux: auditd configured for file access monitoring, or inotify-based watchers
- Baseline entropy values for common file types in the monitored environment
## Workflow
### Step 1: Establish Entropy Baselines
Calculate normal entropy ranges for files in the environment:
```
Entropy Baselines by File Type:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
File Type Normal Entropy Encrypted Entropy
.docx 3.5 - 6.5 7.8 - 8.0
.xlsx 4.0 - 6.8 7.8 - 8.0
.pdf 5.0 - 7.2 7.8 - 8.0
.txt 2.0 - 5.0 7.8 - 8.0
.csv 2.0 - 5.5 7.8 - 8.0
.sql 2.5 - 5.0 7.8 - 8.0
.jpg/.png 7.0 - 7.9 7.9 - 8.0 (hard to distinguish)
.zip/.7z 7.5 - 8.0 7.9 - 8.0 (hard to distinguish)
Key insight: Text-based files show the largest entropy jump when encrypted,
making them the best candidates for entropy-based detection.
```
### Step 2: Implement Real-Time Entropy Monitoring
Monitor file writes and calculate entropy of new content:
```python
import math
from collections import Counter
def shannon_entropy(data):
"""Calculate Shannon entropy of byte data (0.0 to 8.0 scale)."""
if not data:
return 0.0
freq = Counter(data)
length = len(data)
return -sum((c / length) * math.log2(c / length) for c in freq.values())
def is_encryption_entropy(data, threshold=7.5):
"""Check if data entropy indicates encryption."""
entropy = shannon_entropy(data)
return entropy >= threshold, entropy
```
### Step 3: Monitor File System I/O Patterns
Track process-level file operations for ransomware patterns:
```
Ransomware I/O Behavior Signatures:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Rapid sequential file modification:
- >20 files modified per minute by single process
- Read original → Write encrypted → Rename with new extension
- Pattern: CreateFile → ReadFile → WriteFile → CloseHandle → MoveFile
2. File extension changes:
- Original: report.docx → Encrypted: report.docx.locked
- Many extensions changed within short time window
3. Ransom note creation:
- Same text file (README.txt, DECRYPT.html) created in multiple directories
- Created immediately after file encryption in each directory
4. Shadow copy deletion:
- vssadmin.exe delete shadows /all /quiet
- wmic.exe shadowcopy delete
- PowerShell: Get-WmiObject Win32_Shadowcopy | Remove-WmiObject
5. Entropy spike pattern:
- File read: entropy 3.5 (normal document)
- File write: entropy 7.9 (encrypted content)
- Delta > 3.0 is strong ransomware indicator
```
### Step 4: Implement Behavioral Scoring
Combine multiple signals into a composite ransomware score:
```python
def calculate_ransomware_score(process_metrics):
"""Score process behavior for ransomware likelihood (0-100)."""
score = 0
# High file modification rate
files_per_min = process_metrics.get("files_modified_per_minute", 0)
if files_per_min > 50:
score += 30
elif files_per_min > 20:
score += 15
# Entropy increase in written files
avg_entropy_delta = process_metrics.get("avg_entropy_delta", 0)
if avg_entropy_delta > 3.0:
score += 30
elif avg_entropy_delta > 2.0:
score += 15
# File extension changes
extension_changes = process_metrics.get("extension_changes", 0)
if extension_changes > 10:
score += 20
elif extension_changes > 3:
score += 10
# Ransom note creation
if process_metrics.get("ransom_note_created", False):
score += 20
return min(score, 100)
```
### Step 5: Configure Automated Response Thresholds
Set detection thresholds and automated containment actions:
```
Detection Thresholds:
━━━━━━━━━━━━━━━━━━━━
Score 0-25: INFORMATIONAL - Log only, no action
Score 25-50: LOW - Alert SOC for investigation
Score 50-75: HIGH - Alert SOC, suspend process, snapshot VM
Score 75-100: CRITICAL - Kill process, isolate endpoint, alert IR team
Automated Response Actions:
- Suspend/kill the encrypting process
- Disable network adapter to prevent lateral movement
- Create volume shadow copy snapshot before further damage
- Capture process memory dump for forensic analysis
- Send SIEM alert with process details, affected files, and timeline
```
## Verification
- Test detection against known ransomware samples in an isolated sandbox environment
- Verify that entropy monitoring correctly identifies encrypted vs. compressed files
- Confirm that behavioral scoring produces low false-positive rates on normal workloads
- Validate automated response actions execute within acceptable time (under 5 seconds)
- Test with multiple ransomware families (LockBit, BlackCat, Conti) to verify coverage
- Benchmark monitoring overhead to ensure it does not degrade endpoint performance
## Key Concepts
| Term | Definition |
|------|------------|
| **Shannon Entropy** | Mathematical measure of randomness in data (0-8 for bytes); encrypted data approaches 8.0, while text files are typically 2-5 |
| **Differential Entropy** | The change in entropy between a file's original and modified content; a spike indicates encryption |
| **I/O Rate Anomaly** | Abnormally high rate of file read/write operations by a single process, characteristic of bulk encryption |
| **Behavioral Scoring** | Combining multiple weak signals (entropy, I/O rate, file renames) into a composite confidence score |
| **Entropy Evasion** | Techniques used by advanced ransomware to defeat entropy detection, such as Base64 encoding output or partial encryption |
## Tools & Systems
- **Sysmon**: Windows system monitor providing detailed file system and process events for behavioral analysis
- **watchdog (Python)**: Cross-platform file system monitoring library for real-time file change detection
- **psutil (Python)**: Process and system monitoring library for tracking per-process I/O statistics
- **Elastic Endpoint**: Commercial endpoint protection with built-in ransomware behavioral detection using canary files
- **Wazuh**: Open-source security platform with file integrity monitoring and active response capabilities
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# API Reference: Detecting Ransomware Encryption Behavior
## Shannon Entropy
Formula: H(X) = -Sum p(x) log2(p(x)). For byte data range is 0.0 to 8.0.
### Python Implementation
```python
import math
from collections import Counter
def shannon_entropy(data):
freq = Counter(data)
length = len(data)
return -sum((c / length) * math.log2(c / length) for c in freq.values())
```
### Entropy Thresholds
| Range | Interpretation | Example |
|-------|---------------|--------|
| 0.0-1.0 | Nearly uniform | Null files |
| 1.0-4.0 | Low entropy | Plain text |
| 4.0-6.0 | Mixed content | Office docs |
| 6.0-7.0 | Compressed | PDF |
| 7.0-7.5 | Highly compressed | ZIP JPEG |
| 7.5-7.9 | Block cipher encrypted | AES-CBC |
| 7.9-8.0 | Stream cipher encrypted | AES-CTR ChaCha20 |
## psutil Process IO Monitoring
```python
import psutil
proc = psutil.Process(pid)
io = proc.io_counters()
# Fields: read_bytes write_bytes read_count write_count
```
## Sysmon Event IDs
| Event ID | Event | Relevance |
|----------|-------|----------|
| 1 | Process Create | Identify encrypting process |
| 2 | File time changed | Timestomping |
| 11 | FileCreate | Ransom notes |
| 15 | FileCreateStreamHash | ADS usage |
| 23 | FileDelete | Shadow copy deletion |
| 26 | FileDeleteDetected | File deletion |
## Windows ETW Providers
Microsoft-Windows-Kernel-File GUID: EDD08927-9CC4-4E65-B970-C2560FB5C289
| Event ID | Description |
|----------|------------|
| 10 | Create (open) |
| 11 | Close |
| 12 | Read |
| 14 | Write |
| 15 | SetInformation |
## Behavioral Scoring
| Signal | Weight | Threshold |
|--------|--------|-----------|
| Files modified per min | 30 pts | Over 50 |
| Entropy delta | 30 pts | Over 3.0 |
| Extension changes | 20 pts | Over 10 |
| Ransom note creation | 20 pts | Any |
### Score Interpretation
| Score | Severity | Action |
|-------|----------|--------|
| 0-25 | INFO | Log |
| 25-50 | LOW | Alert SOC |
| 50-75 | HIGH | Suspend process |
| 75-100 | CRITICAL | Kill and isolate |
## Shadow Copy Deletion
| Command | Method |
|---------|--------|
| vssadmin delete shadows /all /quiet | VSS Admin |
| wmic shadowcopy delete | WMI |
| bcdedit /set recoveryenabled no | Disable recovery |
| wbadmin delete catalog -quiet | Delete backup |
## watchdog Library
| Method | Trigger |
|--------|--------|
| on_created | File created |
| on_modified | File modified |
| on_deleted | File deleted |
| on_moved | File renamed |
## Double Extension Detection
```python
parts = filename.rsplit(".", 2)
if len(parts) >= 3:
original_ext = "." + parts[-2]
appended_ext = "." + parts[-1]
```
skills/detecting-ransomware-encryption-behavior/scripts/agent.py
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#!/usr/bin/env python3
"""Ransomware encryption behavior detection agent.
Detects ransomware activity using entropy analysis, file system I/O monitoring,
and behavioral heuristics. Monitors file modifications for entropy spikes and
mass rename patterns characteristic of ransomware encryption.
"""
import hashlib
import json
import logging
import math
import os
import sys
import time
from collections import Counter
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
)
logger = logging.getLogger("ransomware_detector")
RANSOMWARE_EXTENSIONS = {
".locked", ".encrypted", ".crypt", ".locky", ".cerber", ".wncry",
".dharma", ".basta", ".blackcat", ".hive", ".royal", ".akira",
".lockbit", ".conti", ".ryuk", ".maze", ".revil", ".phobos",
}
RANSOM_NOTE_NAMES = {
"readme.txt", "readme.html", "decrypt.txt", "decrypt.html",
"how_to_decrypt.txt", "restore_files.txt", "how_to_recover.txt",
}
HIGH_VALUE_EXTENSIONS = {
".docx", ".xlsx", ".pptx", ".pdf", ".doc", ".xls", ".ppt",
".csv", ".sql", ".mdb", ".accdb", ".bak", ".zip", ".7z",
".pst", ".ost", ".eml", ".jpg", ".png", ".dwg", ".vmdk",
}
def shannon_entropy(data):
"""Calculate Shannon entropy of byte data (0.0 to 8.0)."""
if not data:
return 0.0
freq = Counter(data)
length = len(data)
return -sum((c / length) * math.log2(c / length) for c in freq.values())
def analyze_file_entropy(filepath):
"""Analyze entropy of a file and its segments."""
with open(filepath, "rb") as f:
data = f.read()
if not data:
return {"overall": 0.0, "is_encrypted": False}
overall = shannon_entropy(data)
file_size = len(data)
chunk_size = min(4096, file_size // 4) if file_size > 16 else file_size
first_entropy = shannon_entropy(data[:chunk_size])
mid_entropy = shannon_entropy(data[file_size // 2: file_size // 2 + chunk_size])
last_entropy = shannon_entropy(data[-chunk_size:])
return {
"overall": round(overall, 4),
"first_chunk": round(first_entropy, 4),
"mid_chunk": round(mid_entropy, 4),
"last_chunk": round(last_entropy, 4),
"file_size": file_size,
"is_encrypted": overall > 7.5,
"is_partial_encryption": abs(first_entropy - mid_entropy) > 2.0,
}
def scan_directory_entropy(directory, extensions=None):
"""Scan directory for files with high entropy indicating encryption."""
results = {"total_files": 0, "encrypted_files": 0, "files": []}
for root, dirs, files in os.walk(directory):
for filename in files:
filepath = os.path.join(root, filename)
ext = os.path.splitext(filename)[1].lower()
if extensions and ext not in extensions:
continue
try:
analysis = analyze_file_entropy(filepath)
results["total_files"] += 1
if analysis["is_encrypted"]:
results["encrypted_files"] += 1
analysis["path"] = filepath
analysis["filename"] = filename
results["files"].append(analysis)
except (OSError, PermissionError):
continue
results["encryption_ratio"] = (
round(results["encrypted_files"] / results["total_files"], 4)
if results["total_files"] > 0 else 0
)
return results
def detect_ransomware_indicators(directory):
"""Detect multiple ransomware indicators in a directory tree."""
indicators = {
"ransomware_extensions": [],
"ransom_notes": [],
"high_entropy_files": [],
"renamed_files": [],
"score": 0,
}
for root, dirs, files in os.walk(directory):
for filename in files:
filepath = os.path.join(root, filename)
lower_name = filename.lower()
# Check for ransomware file extensions
ext = os.path.splitext(filename)[1].lower()
if ext in RANSOMWARE_EXTENSIONS:
indicators["ransomware_extensions"].append(filepath)
# Check for double extensions (report.docx.locked)
parts = filename.rsplit(".", 2)
if len(parts) >= 3:
original_ext = "." + parts[-2].lower()
appended_ext = "." + parts[-1].lower()
if original_ext in HIGH_VALUE_EXTENSIONS and appended_ext in RANSOMWARE_EXTENSIONS:
indicators["renamed_files"].append({
"path": filepath,
"original_ext": original_ext,
"ransomware_ext": appended_ext,
})
# Check for ransom notes
if lower_name in RANSOM_NOTE_NAMES:
indicators["ransom_notes"].append(filepath)
# Check entropy of high-value file types
if ext in HIGH_VALUE_EXTENSIONS:
try:
analysis = analyze_file_entropy(filepath)
if analysis["is_encrypted"]:
indicators["high_entropy_files"].append({
"path": filepath,
"entropy": analysis["overall"],
})
except (OSError, PermissionError):
continue
# Calculate ransomware score
score = 0
score += min(len(indicators["ransomware_extensions"]) * 5, 30)
score += min(len(indicators["ransom_notes"]) * 15, 30)
score += min(len(indicators["high_entropy_files"]) * 3, 20)
score += min(len(indicators["renamed_files"]) * 5, 20)
indicators["score"] = min(score, 100)
if indicators["score"] >= 75:
indicators["verdict"] = "CRITICAL - Active ransomware encryption detected"
elif indicators["score"] >= 50:
indicators["verdict"] = "HIGH - Strong ransomware indicators present"
elif indicators["score"] >= 25:
indicators["verdict"] = "MEDIUM - Suspicious activity, investigate further"
else:
indicators["verdict"] = "LOW - No significant ransomware indicators"
return indicators
def snapshot_directory_state(directory):
"""Take a baseline snapshot of directory for differential analysis."""
snapshot = {}
for root, dirs, files in os.walk(directory):
for filename in files:
filepath = os.path.join(root, filename)
try:
stat = os.stat(filepath)
sha256 = hashlib.sha256()
with open(filepath, "rb") as f:
for chunk in iter(lambda: f.read(65536), b""):
sha256.update(chunk)
snapshot[filepath] = {
"hash": sha256.hexdigest(),
"size": stat.st_size,
"mtime": stat.st_mtime,
}
except (OSError, PermissionError):
continue
return snapshot
def compare_snapshots(before, after):
"""Compare two directory snapshots to detect bulk encryption."""
changes = {"modified": [], "deleted": [], "created": [], "total_changes": 0}
for path, info in before.items():
if path not in after:
changes["deleted"].append(path)
elif after[path]["hash"] != info["hash"]:
changes["modified"].append({
"path": path,
"size_before": info["size"],
"size_after": after[path]["size"],
})
for path in after:
if path not in before:
changes["created"].append(path)
changes["total_changes"] = (
len(changes["modified"]) + len(changes["deleted"]) + len(changes["created"])
)
if changes["total_changes"] > 0:
mod_ratio = len(changes["modified"]) / max(len(before), 1)
changes["bulk_modification_ratio"] = round(mod_ratio, 4)
changes["ransomware_likely"] = mod_ratio > 0.3 and len(changes["modified"]) > 10
else:
changes["bulk_modification_ratio"] = 0
changes["ransomware_likely"] = False
return changes
if __name__ == "__main__":
print("=" * 60)
print("Ransomware Encryption Behavior Detection Agent")
print("Entropy analysis, I/O monitoring, behavioral heuristics")
print("=" * 60)
if len(sys.argv) < 2:
print("\nUsage:")
print(" python agent.py scan <directory> Scan for ransomware indicators")
print(" python agent.py entropy <directory> Entropy scan of files")
print(" python agent.py entropy-file <file> Analyze single file entropy")
print(" python agent.py snapshot <directory> Take baseline snapshot")
print(" python agent.py compare <snap1> <snap2> Compare two snapshots")
sys.exit(0)
command = sys.argv[1]
if command == "scan":
target = sys.argv[2] if len(sys.argv) > 2 else os.getcwd()
print(f"\n[*] Scanning {target} for ransomware indicators...")
results = detect_ransomware_indicators(target)
print(f"\n--- Ransomware Detection Results ---")
print(f" Score: {results['score']}/100")
print(f" Verdict: {results['verdict']}")
print(f" Ransomware extensions found: {len(results['ransomware_extensions'])}")
print(f" Ransom notes found: {len(results['ransom_notes'])}")
print(f" High-entropy files: {len(results['high_entropy_files'])}")
print(f" Renamed files: {len(results['renamed_files'])}")
print(f"\n{json.dumps(results, indent=2, default=str)}")
elif command == "entropy":
target = sys.argv[2] if len(sys.argv) > 2 else os.getcwd()
print(f"\n[*] Entropy scanning {target}...")
results = scan_directory_entropy(target, HIGH_VALUE_EXTENSIONS)
print(f"\n--- Entropy Scan Results ---")
print(f" Total files scanned: {results['total_files']}")
print(f" Files with encrypted entropy: {results['encrypted_files']}")
print(f" Encryption ratio: {results['encryption_ratio']}")
for f in results["files"][:10]:
print(f" [!] {f['filename']}: entropy={f['overall']}")
elif command == "entropy-file":
if len(sys.argv) < 3:
print("[!] Provide a file path")
sys.exit(1)
filepath = sys.argv[2]
analysis = analyze_file_entropy(filepath)
print(f"\n--- File Entropy Analysis ---")
print(f" File: {filepath}")
print(f" Overall entropy: {analysis['overall']}")
print(f" First chunk: {analysis['first_chunk']}")
print(f" Mid chunk: {analysis['mid_chunk']}")
print(f" Last chunk: {analysis['last_chunk']}")
print(f" Encrypted: {analysis['is_encrypted']}")
print(f" Partial encryption: {analysis['is_partial_encryption']}")
elif command == "snapshot":
target = sys.argv[2] if len(sys.argv) > 2 else os.getcwd()
print(f"\n[*] Taking snapshot of {target}...")
snap = snapshot_directory_state(target)
output = f"snapshot_{int(time.time())}.json"
with open(output, "w") as f:
json.dump(snap, f, indent=2)
print(f"[+] Snapshot saved: {output} ({len(snap)} files)")
elif command == "compare":
if len(sys.argv) < 4:
print("[!] Provide two snapshot JSON files")
sys.exit(1)
with open(sys.argv[2]) as f:
snap1 = json.load(f)
with open(sys.argv[3]) as f:
snap2 = json.load(f)
changes = compare_snapshots(snap1, snap2)
print(f"\n--- Snapshot Comparison ---")
print(f" Modified: {len(changes['modified'])}")
print(f" Deleted: {len(changes['deleted'])}")
print(f" Created: {len(changes['created'])}")
print(f" Ransomware likely: {changes['ransomware_likely']}")
print(f"\n{json.dumps(changes, indent=2, default=str)}")
else:
print(f"[!] Unknown command: {command}")