Detects typosquatting attacks in npm and PyPI package registries by analyzing package name similarity using Levenshtein distance and other string metrics, examining publish date heuristics to identify recently created packages mimicking established ones, and flagging download count anomalies where suspicious packages have disproportionately low usage compared to their legitimate targets. The analyst queries the PyPI JSON API and npm registry API to gather package metadata for automated comparison. Activates for requests involving package typosquatting detection, dependency confusion analysis, malicious package identification, or software supply chain threat hunting in package registries.
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Do not use as the sole determination of malicious intent; name similarity alone does not prove a package is malicious. Do not use for bulk automated takedown requests without manual review of flagged packages. Do not use against private registries without authorization.
requests and python-Levenshtein (or rapidfuzz) packages installedhttps://pypi.org/pypi/<package>/json (PyPI JSON API) and https://registry.npmjs.org/<package> (npm registry API)Establish the set of legitimate packages to monitor for typosquats:
requirements.txt, Pipfile.lock, package.json, or package-lock.json to extract all direct and transitive dependency nameshttps://hugovk.github.io/top-pypi-packages/) or top npm packages by download countre.sub(r"[-_.]+", "-", name).lower()). npm package names are case-sensitive but scoped packages use @scope/name format. Normalize before comparison.Produce potential typosquat variants for each target package:
requests -> rquests, requets, reqests)requests -> erquests, rqeuests, reques ts)requests -> rrquests, requesta)requests -> rrequests, reqquests)my-package -> mypackage, my--package, my_package)python-requests, requests-python, requests2, requests-lib)Check whether generated candidate names actually exist in the registry:
GET https://pypi.org/pypi/<candidate>/json for each candidate. A 200 response means the package exists; 404 means it does not. Extract from the response: info.name, info.version, info.author, info.summary, info.home_page, info.project_urls, and releases (keyed by version with upload_time_iso_8601 timestamps).GET https://registry.npmjs.org/<candidate> with Accept: application/json. Extract: name, description, dist-tags.latest, time.created, time.modified, maintainers, and versions.429 when rate limited; implement exponential backoff.requests.Session) and limit concurrency to avoid triggering abuse protections.Score each existing candidate package against multiple heuristic signals:
pypistats.org/api/); npm provides download counts at https://api.npmjs.org/downloads/point/last-week/<package>.Combine signals into a composite risk score and generate an actionable report:
| Term | Definition |
|---|---|
| Typosquatting | Registering a package name that closely resembles a popular package, exploiting common typos to trick developers into installing malicious code |
| Levenshtein Distance | The minimum number of single-character edits (insertions, deletions, substitutions) required to transform one string into another; the primary metric for measuring name similarity |
| Dependency Confusion | A broader supply chain attack where attackers publish malicious packages to public registries with names matching private internal packages, exploiting package manager resolution order |
| PEP 503 Normalization | The Python packaging specification that treats hyphens, underscores, and periods as equivalent in package names, meaning my-package, my_package, and my.package resolve to the same package |
| QWERTY Distance | A keyboard-layout-aware distance metric measuring how far apart two keys are on a standard keyboard, used to detect substitutions from adjacent key mistyping |
| Combosquatting | A variant of typosquatting where attackers prepend or append common words to a package name (e.g., requests-security, python-requests) |
| StarJacking | An attack where a typosquat package links its repository URL to the legitimate package's GitHub repository to inflate apparent credibility |
https://pypi.org/pypi/<package>/json returning package metadata including name, author, versions, upload timestamps, and project URLshttps://registry.npmjs.org/<package> returning package metadata including maintainers, version history, creation timestamps, and distribution infohttps://api.npmjs.org/downloads/point/<period>/<package> providing download statistics for npm packagesContext: A security team discovers that a developer's workstation was compromised after installing a Python package. The incident response team needs to audit all project dependencies for potential typosquats and establish ongoing monitoring.
Approach:
requirements.txt and Pipfile.lock to extract all 87 direct and transitive dependenciessecurity@pypi.org, and add all 87 dependencies to the ongoing monitoring watchlistPitfalls:
@scope/name) which have different naming rules than unscoped packages## Typosquatting Detection Report
**Scan Date**: 2026-03-19
**Registry**: PyPI
**Packages Monitored**: 87
**Candidates Generated**: 2,412
**Candidates Found in Registry**: 34
**Flagged as Suspicious**: 5
### HIGH Risk (Score >= 70)
| Suspect Package | Target Package | Levenshtein | Created | Downloads | Score |
|----------------|---------------|-------------|---------|-----------|-------|
| reqeusts | requests | 1 | 2026-02-28 | 43 | 92 |
| requsets | requests | 1 | 2026-03-01 | 12 | 88 |
| numpyy | numpy | 1 | 2026-01-15 | 67 | 78 |
### Recommendation
- BLOCK: reqeusts, requsets, numpyy (add to artifact proxy deny-list)
- REPORT: Submit malware reports to security@pypi.org with package names and evidence
- MONITOR: Continue weekly scans for the full dependency watchlist
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概要と使いどころ
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use during authorized red-team credential-access engagements after gaining a foothold or when triaging DPAPI blobs pulled from a host.
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Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target's NT hash without a password reset. Use when BloodHound shows GenericWrite/GenericAll/AddKeyCredentialLink over a target, as a stealthier alternative to ForceChangePassword, during authorized red-team engagements.
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Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
日本語の概要は準備中です。原文の説明を表示しています。
Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.
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Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
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Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.
日本語の概要は準備中です。原文の説明を表示しています。