Skip to content
cccskills
FreePublished on GitHub

measuring-section-entropy-to-detect-packing

Detects packing and encryption by measuring Shannon entropy across a binary's sections and regions: flagging high-entropy executable sections, entropy spikes, and size/raw-vs-virtual anomalies that indicate compression or encryption. Activates for requests to measure entropy, detect packing, or assess whether a sample is compressed/encrypted.

The English summary is being prepared. Showing the original description.

See how to install

Files included (4)

  • SKILL.md3.1 KB
  • LICENSE340 B
  • references/api-reference.md1.3 KB
  • scripts/analyst.py3.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Measuring Section Entropy to Detect Packing

When to Use

  • You want a quick statistical signal of whether a sample is packed or encrypted.
  • You are triaging a PE and need to flag suspicious high-entropy executable sections.
  • You are deciding whether to route a sample to an unpacking workflow.

Do not use entropy as a verdict — legitimate compressed resources and installers also show high entropy; combine with imports, section names, and raw/virtual size anomalies.

Prerequisites

  • Python (math stdlib); optionally pefile for per-section analysis.

Safety & Handling

  • Compute entropy on the inert file; never execute the sample.
  • Keep the sample password-protected at rest and reference it by hash.

Workflow

Step 1: Compute whole-file and sliding-window entropy

Calculate Shannon entropy (0–8 bits/byte). Sliding-window entropy reveals localized high-entropy regions even when the overall value is moderate.

python scripts/analyst.py entropy sample.exe

Step 2: Compute per-section entropy (PE)

For each PE section, compute entropy and compare raw vs. virtual size. Executable sections with entropy > ~7.0 are a packing indicator.

Step 3: Correlate structural anomalies

Flag classic packer signs: high-entropy .text, unusual/renamed sections (UPX0, random names), tiny raw size but large virtual size, and an entry point outside .text.

Step 4: Decide routing

If indicators stack (high entropy + thin IAT + odd sections), route to unpacking; otherwise proceed with normal static analysis.

Validation

  • High-entropy executable sections are corroborated by other packing signs before concluding.
  • Benign high-entropy cases (compressed resources) are not misclassified as packers.
  • The routing decision (unpack vs. proceed) is justified by combined indicators.

Pitfalls

  • Calling any high-entropy file "packed" without structural corroboration.
  • Ignoring sliding-window entropy and missing a localized encrypted blob.
  • Overlooking raw-vs-virtual size mismatch, a strong unpacking-at-runtime hint.

References

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Reverses Android malware: unpacking APKs, decompiling DEX bytecode to readable Java, auditing the manifest for abused permissions and components, and locating dynamically loaded or native payloads. Activates for requests to analyze an APK, decompile DEX, or investigate a suspicious Android app.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox API logs, or identify behavior from Win32/Native API calls.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes Windows Authenticode signatures on PE files: checking for a signature, reading the signer certificate chain, detecting revoked/expired/stolen certs, and recognizing signature-stripping and catalog-signing abuse. Activates for requests to analyze a code signature, verify Authenticode, or assess signer trust on a PE.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes banking trojan webinject configurations to extract targeted institutions, injected JavaScript/HTML, and data-theft hooks, mapping the fraud workflow. Activates for requests to analyze banking trojan webinjects, parse a webinject config, or map targeted banks and credential-theft injections.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes Python-based malware packaged as PyInstaller/py2exe executables by detecting the packer, locating the embedded archive, and identifying bundled .pyc modules for extraction and decompilation. Activates for requests to analyze compiled Python malware, unpack a PyInstaller binary, or extract pyc modules from a frozen Python executable.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes cryptojacking/coinminer malware by extracting mining pool endpoints (stratum), wallet addresses, algorithm and miner identifiers (XMRig and forks), and persistence or resource-control settings from static strings and config. Activates for requests to analyze a cryptominer, extract mining pool and wallet IOCs, or identify coinminer configuration.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

See all skills by meltedinhex

Report a problem with this skill