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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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@@ -17,3 +17,322 @@ license: Apache-2.0
Detect timestamp manipulation by analyzing NTFS MFT entries for
discrepancies between $STANDARD_INFORMATION and $FILE_NAME attributes.
## When to Use
- Investigating suspected anti-forensic activity where an adversary may have altered file timestamps to blend malware into legitimate directories
- Threat hunting for defense evasion (MITRE ATT&CK T1070.006) across compromised Windows systems
- Validating timeline integrity during forensic examinations of disk images or live acquisitions
- Triaging suspicious files that appear to have creation dates older than the OS installation or inconsistent with known deployment timelines
- Detecting tools like Timestomp (Metasploit), NTimeStomp, SetMACE, or PowerShell Set-ItemProperty used to alter timestamps
- Building automated detection pipelines that flag temporal anomalies in MFT data for SOC analysts
**Do not use** as the sole detection method; advanced adversaries can manipulate both $STANDARD_INFORMATION and $FILE_NAME timestamps (though the latter requires raw disk access and is much harder). Combine with USN Journal, $LogFile, and ShimCache/Amcache analysis for corroboration.
## Prerequisites
- Raw $MFT file extracted from a Windows system (via FTK Imager, KAPE, or live extraction)
- `MFTECmd` (Eric Zimmerman tool) or `analyzeMFT` for MFT parsing
- Python 3.8+ with `pandas` for analysis
- Optional: `mft` Python library (`pip install mft`) for programmatic MFT parsing
- Optional: KAPE (Kroll Artifact Parser and Extractor) for automated artifact collection
- Timeline Explorer or Excel for visual analysis of parsed MFT output
## Workflow
### Step 1: Extract the $MFT from a Live System or Disk Image
```powershell
# Method 1: Using KAPE to collect MFT and related artifacts
.\kape.exe --tsource C: --tdest D:\Evidence\MFT_Collection --target !SANS_Triage
# Method 2: Using FTK Imager CLI to extract $MFT
ftkimager.exe \\.\C: D:\Evidence\mft_raw.bin --e01 --include $MFT
# Method 3: Raw copy using RawCopy (handles locked NTFS system files)
RawCopy.exe /FileNamePath:C:0 /OutputPath:D:\Evidence\ /OutputName:$MFT
```
```bash
# Method 4: On a mounted forensic image in Linux
sudo mount -o ro,norecovery /dev/sdb1 /mnt/evidence
sudo icat -o 2048 /dev/sdb 0 > /mnt/output/$MFT
# Method 5: Using sleuthkit to extract MFT from disk image
icat -o 2048 evidence.E01 0 > extracted_MFT
```
### Step 2: Parse the MFT with MFTECmd
Use Eric Zimmerman's MFTECmd to produce a CSV with both $STANDARD_INFORMATION and $FILE_NAME timestamps:
```powershell
# Parse MFT to CSV with all timestamp columns
MFTECmd.exe -f "D:\Evidence\$MFT" --csv D:\Evidence\Parsed\ --csvf mft_parsed.csv
# The output CSV contains these critical columns:
# Created0x10 - $STANDARD_INFORMATION Created timestamp
# LastModified0x10 - $STANDARD_INFORMATION Modified timestamp
# LastAccess0x10 - $STANDARD_INFORMATION Accessed timestamp
# LastRecordChange0x10 - $STANDARD_INFORMATION Entry Modified timestamp
# Created0x30 - $FILE_NAME Created timestamp
# LastModified0x30 - $FILE_NAME Modified timestamp
# LastAccess0x30 - $FILE_NAME Accessed timestamp
# LastRecordChange0x30 - $FILE_NAME Entry Modified timestamp
```
### Step 3: Detect Timestomping via SI vs FN Comparison
The core detection: $STANDARD_INFORMATION timestamps are easily modified by user-mode tools, but $FILE_NAME timestamps are updated only by the NTFS driver (kernel-mode). When SI timestamps are OLDER than FN timestamps, timestomping is likely:
```python
import pandas as pd
from datetime import datetime, timedelta
def load_mft_data(csv_path):
"""Load MFTECmd parsed CSV output."""
df = pd.read_csv(csv_path, low_memory=False)
# Parse timestamp columns
timestamp_cols = [
"Created0x10", "LastModified0x10", "LastAccess0x10", "LastRecordChange0x10",
"Created0x30", "LastModified0x30", "LastAccess0x30", "LastRecordChange0x30"
]
for col in timestamp_cols:
if col in df.columns:
df[col] = pd.to_datetime(df[col], errors="coerce")
return df
def detect_timestomping(df):
"""Detect timestamp manipulation by comparing SI and FN attributes.
Key indicators:
1. SI Created < FN Created (SI timestamp pushed back in time)
2. SI timestamps have nanoseconds = 0000000 (tool artifact)
3. SI Created < FN Entry Modified (impossible under normal NTFS behavior)
4. Large gap between SI and FN timestamps
"""
results = []
for idx, row in df.iterrows():
si_created = row.get("Created0x10")
fn_created = row.get("Created0x30")
si_modified = row.get("LastModified0x10")
fn_modified = row.get("LastModified0x30")
si_entry = row.get("LastRecordChange0x10")
fn_entry = row.get("LastRecordChange0x30")
if pd.isna(si_created) or pd.isna(fn_created):
continue
filepath = row.get("FileName", "unknown")
parent_path = row.get("ParentPath", "")
full_path = f"{parent_path}\\{filepath}" if parent_path else filepath
indicators = []
# Detection 1: SI Created is BEFORE FN Created
# Under normal NTFS operations, SI Created >= FN Created
if si_created < fn_created:
delta = fn_created - si_created
indicators.append({
"check": "SI_Created < FN_Created",
"si_value": str(si_created),
"fn_value": str(fn_created),
"delta": str(delta),
"confidence": "high"
})
# Detection 2: SI Modified is BEFORE FN Created
# A file cannot be modified before it was created
if pd.notna(si_modified) and si_modified < fn_created:
indicators.append({
"check": "SI_Modified < FN_Created",
"si_value": str(si_modified),
"fn_value": str(fn_created),
"confidence": "high"
})
# Detection 3: Nanosecond precision check
# Many timestomping tools set timestamps with zero nanoseconds
if pd.notna(si_created):
si_created_str = str(si_created)
if ".000000" in si_created_str or si_created_str.endswith("00:00:00"):
# Check if FN has normal nanosecond precision
fn_str = str(fn_created)
if ".000000" not in fn_str:
indicators.append({
"check": "SI_nanoseconds_zeroed",
"si_value": si_created_str,
"fn_value": fn_str,
"confidence": "medium"
})
# Detection 4: Large time gap between SI and FN
# Normal gap is seconds to minutes, not years
if abs((si_created - fn_created).days) > 365:
indicators.append({
"check": "SI_FN_gap_exceeds_1_year",
"si_value": str(si_created),
"fn_value": str(fn_created),
"delta_days": abs((si_created - fn_created).days),
"confidence": "high"
})
# Detection 5: SI Entry Modified much later than SI Created
# Indicates the SI attribute was rewritten
if pd.notna(si_entry) and pd.notna(si_created):
entry_delta = si_entry - si_created
if entry_delta.days > 365 * 5: # Entry modified years after creation
indicators.append({
"check": "SI_entry_modified_years_after_creation",
"si_created": str(si_created),
"si_entry_modified": str(si_entry),
"confidence": "medium"
})
if indicators:
results.append({
"file_path": full_path,
"entry_number": row.get("EntryNumber", ""),
"in_use": row.get("InUse", True),
"si_created": str(si_created),
"fn_created": str(fn_created),
"indicators": indicators,
"highest_confidence": max(i["confidence"] for i in indicators),
})
return results
# Run detection
df = load_mft_data("D:\\Evidence\\Parsed\\mft_parsed.csv")
stomped_files = detect_timestomping(df)
print(f"\nTimestomping Detection Results")
print(f"{'='*60}")
print(f"Total MFT entries analyzed: {len(df)}")
print(f"Suspicious entries found: {len(stomped_files)}")
print()
for entry in sorted(stomped_files, key=lambda x: x["highest_confidence"], reverse=True):
print(f"[{entry['highest_confidence'].upper()}] {entry['file_path']}")
print(f" SI Created: {entry['si_created']}")
print(f" FN Created: {entry['fn_created']}")
for ind in entry["indicators"]:
print(f" Check: {ind['check']} (confidence: {ind['confidence']})")
print()
```
### Step 4: Corroborate with USN Journal Analysis
The USN Journal records metadata change events that persist even after timestomping:
```python
def correlate_with_usn_journal(stomped_files, usn_csv_path):
"""Cross-reference timestomped files with USN Journal entries.
The USN Journal records a BASIC_INFO_CHANGE reason when timestamps
are modified, providing corroborating evidence of timestomping.
"""
usn_df = pd.read_csv(usn_csv_path, low_memory=False)
usn_df["UpdateTimestamp"] = pd.to_datetime(usn_df["UpdateTimestamp"], errors="coerce")
corroborated = []
for entry in stomped_files:
filename = entry["file_path"].split("\\")[-1]
# Find USN entries for this file with BASIC_INFO_CHANGE
usn_matches = usn_df[
(usn_df["Name"] == filename) &
(usn_df["UpdateReasons"].str.contains("BASIC_INFO_CHANGE", na=False))
]
if not usn_matches.empty:
entry["usn_corroboration"] = True
entry["usn_change_times"] = usn_matches["UpdateTimestamp"].tolist()
entry["highest_confidence"] = "critical"
corroborated.append(entry)
print(f"[CORROBORATED] {filename} - USN Journal confirms "
f"BASIC_INFO_CHANGE at {usn_matches['UpdateTimestamp'].iloc[0]}")
return corroborated
# Parse USN Journal (use MFTECmd or ANJP)
# MFTECmd.exe -f "$J" --csv D:\Evidence\Parsed\ --csvf usn_parsed.csv
```
### Step 5: Check ShimCache and Amcache for Timeline Validation
```python
def check_shimcache_timeline(stomped_files, shimcache_csv):
"""Validate timestamps against ShimCache (AppCompatCache) entries.
ShimCache records the last modification time of executables
independently of NTFS timestamps, providing another corroboration point.
"""
shim_df = pd.read_csv(shimcache_csv, low_memory=False)
shim_df["LastModifiedTimeUTC"] = pd.to_datetime(
shim_df["LastModifiedTimeUTC"], errors="coerce"
)
for entry in stomped_files:
filepath = entry["file_path"]
shim_match = shim_df[
shim_df["Path"].str.lower() == filepath.lower()
]
if not shim_match.empty:
shim_time = shim_match["LastModifiedTimeUTC"].iloc[0]
si_modified = pd.to_datetime(entry.get("si_created"))
if pd.notna(shim_time) and pd.notna(si_modified):
delta = abs((shim_time - si_modified).days)
if delta > 30:
entry["shimcache_mismatch"] = True
entry["shimcache_time"] = str(shim_time)
print(f"[SHIMCACHE MISMATCH] {filepath}")
print(f" SI timestamp: {si_modified}")
print(f" ShimCache timestamp: {shim_time}")
print(f" Delta: {delta} days")
return stomped_files
```
### Step 6: Generate a Timestomping Detection Report
```python
import json
def generate_report(stomped_files, output_path):
"""Generate a structured JSON report of all timestomping detections."""
report = {
"report_title": "Timestomping Detection Analysis",
"generated_at": datetime.utcnow().isoformat() + "Z",
"mitre_technique": "T1070.006 - Indicator Removal: Timestomp",
"total_suspicious_files": len(stomped_files),
"critical_findings": len([f for f in stomped_files if f["highest_confidence"] == "critical"]),
"high_findings": len([f for f in stomped_files if f["highest_confidence"] == "high"]),
"medium_findings": len([f for f in stomped_files if f["highest_confidence"] == "medium"]),
"findings": stomped_files,
}
with open(output_path, "w") as f:
json.dump(report, f, indent=2, default=str)
print(f"Report written to {output_path}")
print(f" Critical: {report['critical_findings']}")
print(f" High: {report['high_findings']}")
print(f" Medium: {report['medium_findings']}")
generate_report(stomped_files, "D:\\Evidence\\timestomping_report.json")
```
## Verification
- Confirm MFTECmd parses the $MFT without errors and produces both 0x10 (SI) and 0x30 (FN) timestamp columns
- Create a test file and use a timestomping tool (e.g., NTimeStomp) in a lab to verify the detection logic catches the manipulation
- Validate that the nanosecond-zeroed check does not produce excessive false positives on files created by installers that legitimately set timestamps
- Cross-reference flagged files with the USN Journal to confirm BASIC_INFO_CHANGE events exist at the expected times
- Verify ShimCache and Amcache timestamps provide independent corroboration of timeline inconsistencies
- Test against known-clean system images to establish a false-positive baseline (some backup/imaging software legitimately resets timestamps)
- Confirm the detection pipeline correctly handles deleted MFT entries (InUse=false) which may contain evidence of timestomped files that were later removed