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Hackers Leverage Malicious PyPI Package to Attack Users and Steal Cryptocurrency Details

By CyberDudeBivash

• Updated Today • High-Severity Supply-Chain Threat

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SUMMARY

A malicious Python package on the official PyPI registry is being used by threat actors to deploy crypto-stealing malware, harvest wallet keys, intercept sensitive API tokens, and exfiltrate browser-stored seed phrases. The package impersonates legitimate libraries, performs obfuscated post-install execution, and communicates with attacker-controlled C2 servers using encrypted payloads. This is a live supply-chain attack impacting developers, security teams, and crypto investors globally.

Introduction: Why PyPI is Under Constant Attack
How the Fake Package Was Discovered
Technical Breakdown of the Malicious Code
Post-Install Script Execution and Abuse
The Crypto Theft Mechanism Explained
Browser Credential Extraction Process
Seed Phrase Harvesting and API Token Theft
Network & C2 Infrastructure Analysis
IOC List for SOC Teams
Risk to Developers, Startups & Enterprises
Mitigation Steps & Secure Python Guidelines
How to Audit Your Systems for Infection
CyberDudeBivash Recommended Tools
FAQ – Everything You Must Know
References & Further Reading

Introduction: Why PyPI Is Under Constant Attack

In recent years, the Python Package Index (PyPI) has become a lucrative target for threat actors who understand that a single compromised or malicious package can reach tens of thousands of developers instantly. Unlike traditional malware, supply-chain infections propagate not through phishing emails or drive-by downloads, but through trust. Developers install packages under the assumption that open-source registries enforce sanitization, author validation, and malicious code screening.

The reality is far more dangerous: PyPI repositories are continuously abused due to automated account creation, weak maintainer hygiene, stolen developer credentials, and upload automation that allows attackers to push new malicious packages within seconds. The newly discovered crypto-stealer package leverages all of these weaknesses, targeting users who perform pip installs during CI pipelines, local testing, or development setup.

How the Fake Package Was Discovered

Security researchers first detected the malicious package after noticing unusual installation-time network calls originating from Python environments that had recently pulled a seemingly legitimate library. The package name closely resembled a popular open-source utility, a common typosquatting technique used to trick developers into installing an impostor dependency.

Once downloaded, the package executed hidden scripts that communicated with an attacker-controlled server. A deeper examination revealed that the uploaded version only appeared benign; its internal directories concealed obfuscated Python modules designed to harvest sensitive data from victim machines.

Package Structure Analysis

A typical legitimate PyPI package includes metadata files such as setup.py, PKG-INFO, LICENSE, and the module directory. However, the malicious version embedded multiple layers of deception:

Hidden directories using dot-prefix naming conventions
Obfuscated .pyc files disguised as resources
Base64-wrapped payloads that unpack during installation
Encrypted configuration blocks pointing to C2 servers
A modified setup.py script designed to trigger silent execution

The most alarming component was the use of post-install execution hooks inside setup.py. When pip installs the package, Python’s distutils or setuptools framework allows packages to run code before, during, or after installation. Attackers exploit this feature to deploy malware without requiring manual execution by the victim.

Post-Install Execution Abuse

The malicious package hijacks the setup.py file to register a custom install command. As soon as the package is installed, the embedded function executes automatically. This is an extremely dangerous technique since many developers never manually open or review the contents of setup.py before installing dependencies.

The package’s post-install script performs the following operations:

Creates a temporary working directory inside the user’s home folder.
Decodes a Base64-encoded executable payload.
Writes the binary to disk with a random file name.
Executes the binary as a background process via Python’s subprocess module.
Deletes visible traces of the package directory to avoid detection.

This sequence ensures the malware persists and continues running long after the installation completes, making it highly effective in both developer machines and CI/CD environments.

Crypto Theft Mechanism

The primary objective of this malicious package is to steal cryptocurrency-related information. Crypto wallets typically store sensitive data such as private keys, seed phrases, vault passphrases, and login tokens either in local browser storage, application files, or remote password managers. The attacker-developed stealer includes modules that scan and exfiltrate:

Bitcoin and Ethereum wallet seed phrases
MetaMask browser extension data
Phantom, Coinbase Wallet, and Keplr browser-based storage
WalletConnect session tokens
API keys used in crypto trading bots
Exchanges like Binance, Coinbase, Kraken, Bybit, OKX

The stealer collects these artifacts by targeting LevelDB files, SQLite databases, JSON configs, and browser local storage folders that are commonly used by wallet extensions.

Browser Credential Extraction

One of the most disturbing capabilities observed in the malware is its deep integration with Chromium and Firefox-based browser storage locations. Browsers store login credentials, cookie jars, and session tokens in portable formats. Crypto extensions often leave sensitive information accessible to local processes.

The stealer uses two key functions:

Direct file reading to scrape authentication data.
Decryption routines to unlock browser-stored secrets.

Chromium-based browsers encrypt local passwords using OS-level APIs (Windows DPAPI, macOS Keychain, Linux libsecret). The malware includes implementations to bypass or utilize these APIs to decrypt sensitive password blobs. This allows it to extract:

Exchange passwords
2FA backup codes
Crypto wallet private keys
Account recovery phrases

Once decrypted, the data is compressed, encrypted, and sent to the attacker’s command-and-control server.

Seed Phrase Harvesting

Seed phrases are the ultimate target for attackers because they allow full control over a victim’s cryptocurrency holdings. The malware uses multiple scanning routines to locate files or strings containing 12-word and 24-word mnemonic patterns. It also inspects clipboard history and monitors real-time user inputs if the system keyboard hooks are supported.

API Token & Developer Secrets Theft

Crypto trading bots, Node.js services, and Python scripts often store API keys in environment variables, config files, .env files, or YAML manifests. The malware scans for known key patterns such as:

BINANCE_API_KEY
COINBASE_KEY
KRAKEN_SECRET
WEB3_PROVIDER
INFURA_PROJECT_ID

These keys are invaluable for account takeover attacks, unauthorized trading, and draining of crypto wallets.

Command-and-Control (C2) Infrastructure

The malware communicates with remote servers using encrypted HTTPS requests. The domains used by the attackers rotate frequently, leveraging:

Compromised WordPress sites
Bulletproof hosting providers
Content Delivery Networks acting as relays
Obfuscated IP resolution via DNS-over-HTTPS

The package includes hardcoded fallback URLs that activate if the primary C2 becomes unavailable. This persistence mechanism ensures continued operation even when takedowns occur.

IOC List for SOC Teams

Below are indicators of compromise observed during analysis. SOC teams should ingest these into SIEM platforms for active hunting.

Suspicious package names mimicking common Python libraries
Base64-encoded executables inside setup.py files
Unexpected network traffic during or after pip installs
Outbound connections to unknown HTTPS domains immediately after installation
Strange Python processes running silently in the background
Unauthorized access to browser-level databases

Risk to Developers, Startups, and Enterprises

This type of supply-chain attack is among the most dangerous for modern companies because it infiltrates systems before code is even executed. Any CI/CD pipeline that automatically installs dependencies using pip is at risk of becoming a vector for lateral movement, credential theft, and compromise of cloud workloads.

Startups that rely heavily on crypto integrations and blockchain development are particularly vulnerable. Attackers know that developers often hold seed phrases and wallet keys for testing or deployment, making them high-value targets.

Mitigation Steps and Secure Python Guidelines

To defend against malicious PyPI packages, developers must employ strict dependency hygiene. Recommended protections include:

Use pip install –no-binary :all: for local source review
Pin dependency versions using hash-based verification
Maintain a private Python package mirror
Audit setup.py files for suspicious commands
Use virtual environments isolated from host systems
Integrate SAST and dependency scanning into CI pipelines
Use trusted package sources only

Enterprises must deploy automated dependency security scanners to identify malicious uploads before they reach development environments.

How to Audit Your System for Infection

Security teams and developers can perform the following steps to determine if the malicious package has infected their machines:

List recently installed Python packages and compare versions
Search for hidden directories and obfuscated .pyc files
Inspect browser storage for unauthorized access attempts
Review network logs for suspicious outbound domains
Check environment variables for modifications
Scan the system using EDR and open-source tools

If an infection is found, rotate all API keys immediately, transfer crypto assets to a clean wallet, and reinstall the Python environment from scratch using verified packages.

CyberDudeBivash Recommended Tools for Protecting Against Supply-Chain Malware

To defend users, developers, and enterprises from malicious PyPI packages and crypto-targeting threats, we recommend a combination of automated scanning, endpoint protection, and manual review. Below are tools vetted by the CyberDudeBivash team based on real-world performance and threat detection capabilities.

Static Application Security Testing (SAST) scanners for Python
Dependency vulnerability scanners integrated with CI/CD
EDR tools capable of detecting post-install script execution
Network monitoring systems for detecting suspicious outbound traffic
Browser security tools that block unauthorized access to credential stores

CyberDudeBivash Security Apps and Tools

Cephalus Hunter – RDP Hijack Detector and Malware IOC Scanner
CyberDudeBivash Threat Analyzer – Python-based Threat Intelligence Toolkit
Wazuh Ransomware Detection Rules – Windows and Linux Editions
CyberDudeBivash DFIR Triage Toolkit – Incident Response Automation
Browser Session Shield – Session Hijack Defender

All these tools are available on our official apps hub:
CyberDudeBivash Apps and Products Hub

Recommended by CyberDudeBivash

30–60–90 Day Security Action Plan

Day 1–30: Immediate Controls

Audit all Python dependencies in development and production environments
Conduct a full review of setup.py files across internal packages
Rotate all cryptocurrency-related secrets
Deploy EDR solutions and enforce browser protection policies
Review CI/CD logs for unauthorized installations

Day 31–60: Intermediate Hardening

Deploy a private PyPI mirror with internal verification workflows
Implement hash pinning and signed package validation
Introduce strict RBAC for access to deployment pipelines
Begin training developers on dependency security hygiene

Day 61–90: Long-Term Supply Chain Defense

Automate dependency scanning across all repositories
Adopt Software Bill of Materials (SBOM) enforcement
Introduce real-time monitoring for credential access attempts
Develop a crypto-security incident response playbook
Integrate threat intelligence feeds into SIEM workflows

Frequently Asked Questions

How do attackers upload malicious packages to PyPI?

Attackers leverage automated tooling, stolen maintainer credentials, weak authentication mechanisms, and typosquatting to push malicious libraries that blend into legitimate package listings.

Can pip install automatically execute malware?

Yes. If setup.py contains a custom install command, it can execute arbitrary code during installation without user interaction.

Which developers are most at risk?

Crypto developers, AI engineers, blockchain testers, and backend engineers handling financial systems are primary targets.

How can organizations protect themselves?

By enforcing dependency vetting, scanning packages, using private mirrors, auditing installation logs, and isolating build environments.

What should be done if infection is suspected?

Rotate seeds and API keys, migrate crypto to a fresh wallet, reinstall Python environments, and perform a complete threat-hunting cycle.

References

PyPI Security Documentation
OWASP Software Supply Chain Security
MITRE ATT&CK Techniques for Dependency Abuse
Independent Malware Research Reports
CyberDudeBivash ThreatWire Intelligence Logs

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