The serpent’s tongue: Luring the Python out of its den

1. Executive summary

Cisco Talos published a technical analysis detailing how threat actors abuse native Python package installation mechanisms to execute malicious payloads and establish persistence on developer endpoints. The research highlights a 69% year-over-year increase in published malware advisories, with 17% of reviewed GitHub Advisory Database entries targeting the Pip ecosystem. The threat actor group "TeamPCP" is cited as having utilized Python module supply chain attacks to compromise Microsoft's GitHub subsidiary across 20 "waves" of attacks; however, TeamPCP has no MITRE ATT&CK profile, and this attribution remains unconfirmed. EMEA financial services clients with active Python development, data science, or AI/ML pipelines face elevated risk of developer endpoint compromise, credential theft, and downstream CI/CD infrastructure infiltration via these native packaging abuses.

2. Regulatory framing

3. Technical analysis & attack chain

The attack chain relies on the inherent trust placed in Python package installation processes. The following steps outline the confirmed lifecycle of a malicious package compromise based on the Talos research:

1. Hosting and Delivery: An adversary publishes a malicious package to PyPI, a custom web server, or a VCS repository. The package is formatted as a source distribution (sdist) or wheel.
2. Installation Trigger: A developer installs the package using pip or a similar tool (e.g., poetry, uv). The package manager downloads and processes the distribution file.
3. Payload Execution: The malicious payload executes automatically during the build or installation phase without requiring the developer to directly interact with or import the code.
4. Persistence or Beaconing: The payload establishes a foothold. Depending on the technique used, this may be a transient beacon to a third-party domain or a persistent mechanism that executes on every subsequent Python invocation.
5. Operationalization: Stolen assets or established access are operationalized by the adversary. Talos notes recent trends indicate a dwell time of nine days, with exfiltration achievable within an hour of installation.

Technical specifics of abuse mechanisms

The research categorizes native Python feature abuses into two main areas: Build Hook Abuses and Package Content Abuses.

Build Hook Abuses

• setup.py Command Class Override: The setup function uses distutils command classes. Adversaries override installation behavior using malicious mock-objects (e.g., BeaconOnInstall). The initial payload executes once during package installation and beacons third-party domains. Persistence is transient.
• Path Configuration Files (.pth): .pth files placed directly under site-packages or dist-packages can execute Python one-liners. They execute with every invocation of Python, providing persistent execution. Adversaries use distutils command classes in setup.py to convert a hidden payload (e.g., in a .md file) to a .pth file. Alternatively, if the Hatchling backend is used, tool.hatch.build.targets.wheel.force-include in pyproject.toml drops the .pth file. The threat actor TeamPCP (attribution unconfirmed) reportedly used this technique in the supply chain compromise of the litellm package.
• Site Customization Hooks: Python's site module executes sitecustomize.py and usercustomize.py files located in sys.path directories. Adversaries drop these files into package folders. The VIPERTUNNEL backdoor was reported to abuse site hooks to import and trigger DLL execution. In Talos testing, dropping sitecustomize.py caused a curl command to attempt connections to www.google.com whenever pip freeze was executed.
• PYTHONPATH Manipulation: Adversaries alter the user profile to manipulate the PYTHONPATH environment variable. This points Python to adversary-controlled directories, executing malicious payloads (like sitecustomize.py) on every new shell session. This technique is limited to source distributions using setup.py.

Package Content Abuses

• Init Files (__init__.py): Malicious payloads hidden in __init__.py files execute when a module within the same folder is imported. This provides conditional persistence. This technique was reportedly used in the lightning supply chain compromise.
• Main Files (__main__.py): Packages executed as scripts via python -m <package> use __main__.py as an entry point. Adversaries hide payloads here. In Talos testing, the redpy_demo package's __main__.py executed the netstat command via the subprocess module.
• Entry Point Hijacking: Packages declare entry points (e.g., in project.scripts in pyproject.toml) that create binaries in the environment's binary/scripts folder. Adversaries declare aliases like netstat to override legitimate binaries due to higher search path ranking. The malicious binary executes its payload while running the legitimate binary in the background.
• Package Directory Collisions: Different distributions can use the same name for packaging directories. When installed, their contents extract to the same folder, with overlapping files replacing existing ones. Adversaries create fraudulent projects that override legitimate functions (e.g., overriding the read_json function of the pandas library) with malicious ones.

Confidence caveat: The attribution of the litellm compromise to TeamPCP and the use of site hooks by VIPERTUNNEL are single-sourced claims from the Talos blog post. TeamPCP has no MITRE ATT&CK profile; treat this attribution as unconfirmed. Verify before enforcement.

4. Mitigation & containment

P1 — Within 24 hours: Containment & Immediate Controls

• Audit Active Environments: Run pip-audit --fix on all active development, CI/CD, and production Python environments to identify and automatically upgrade vulnerable or known-malicious dependencies to minimum safe versions.
• Isolate High-Risk Packages: If litellm, lightning, or redpy_demo are present in any environment, isolate the affected endpoints and review for .pth file persistence or unauthorized sitecustomize.py/usercustomize.py files.
• Block Suspicious Indicators: Block network egress for Python processes making unexpected curl or netstat subprocess calls to external domains (e.g., www.google.com used in testing).

P2 — Within 72 hours: Remediation & Hardening

• Enforce Version Pinning: Ensure all Python installations are reproducible. Pin exact dependency versions and verify hashes to prevent "dependency drift" and tampering.
• Restrict Installation Sources: Configure pip to only allow installation from approved, internal repositories. Disable the use of --extra-index-url and untrusted custom web servers. Enforce this via PIP_CONFIG_FILE and restrict the PIP_FIND_LINKS environment variable.
• Scan for Persistence Mechanisms: Search all site-packages and dist-packages directories for unauthorized .pth files, sitecustomize.py, and usercustomize.py files. Review PYTHONPATH environment variables in user profiles for unauthorized modifications.
• Review Entry Points: Audit project.scripts in pyproject.toml files and installed binaries in environment bin/ or Scripts/ directories for hijacked aliases (e.g., netstat).

P3 — Within 7 days: Strategic Defenses

• Implement Behavioral Analysis: Deploy EDR rules to monitor Python processes for suspicious child process execution (e.g., python.exe spawning calc.exe, curl.exe, or netstat.exe).
• Establish Package Auditing Pipelines: Integrate automated dependency auditing and static analysis into CI/CD pipelines to intercept malicious packages before deployment.
• Developer Awareness Training: Educate developers on the risks of setup.py auto-execution and the dangers of installing unverified packages from PyPI or VCS repositories.

5. Indicators of compromise

No indicators of compromise available in the source material.

6. Detection

rule Malicious_Python_Package_Techniques {
    meta:
        author = "Adverse Trace"
        date = "2026-07-14"
        reference = "https://blog.talosintelligence.com/the-serpents-tongue-luring-the-python-out-of-its-den/"
        description = "Detects Python package abuse techniques including setup.py command class overrides, .pth persistence, and entry point hijacking"
    strings:
        $setup_beacon = "BeaconOnInstall" ascii
        $pth_file = ".pth" ascii
        $sitecustomize = "sitecustomize.py" ascii
        $usercustomize = "usercustomize.py" ascii
        $force_include = "tool.hatch.build.targets.wheel.force-include" ascii
        $entry_point_netstat = "netstat" ascii
        $redpy_demo = "redpy_demo" ascii
        $init_py = "__init__.py" ascii
        $main_py = "__main__.py" ascii
        $pythonpath = "PYTHONPATH" ascii
    condition:
        3 of them
}

title: Suspicious Python Package Installation Behavior
id: 7a1c2d3e-4f5a-6b7c-8d9e-0f1a2b3c4d5e
status: experimental
description: Detects Python processes spawning suspicious child processes or modifying environment variables indicative of malicious package installation techniques.
author: Adverse Trace
date: 2026/07/14
references:

    - https://blog.talosintelligence.com/the-serpents-tongue-luring-the-python-out-of-its-den/
logsource:
    product: windows
    category: process_creation
detection:
    selection_payload_execution:
        ParentImage|endswith:

            - '\python.exe'
            - '\pip.exe'
        Image|endswith:

            - '\calc.exe'
            - '\curl.exe'
            - '\netstat.exe'
    selection_persistence_files:
        CommandLine|contains:

            - 'sitecustomize.py'
            - 'usercustomize.py'
            - '.pth'
    selection_env_modification:
        CommandLine|contains:

            - 'PYTHONPATH'
    condition: selection_payload_execution or selection_persistence_files or selection_env_modification
falsepositives:

    - Legitimate Python development tools modifying environment variables or executing network utilities.
level: high

7. Sources

• Cisco Talos, "The serpent's tongue: Luring the Python out of its den", https://blog.talosintelligence.com/the-serpents-tongue-luring-the-python-out-of-its-den/, 2026-07-14

8. Adverse Trace position

Adverse Trace assesses the risk from malicious Python package installations as high for EMEA financial services clients with active software development, data science, or AI/ML operations. The techniques documented by Talos are reliable, leverage native functionality, and require no exploitation of underlying OS vulnerabilities—only the inherent trust placed in Python package managers. The reported nine-day dwell time and rapid exfiltration window necessitate proactive dependency management. The attribution of specific campaigns to TeamPCP is unconfirmed (single-sourced; no MITRE ATT&CK profile) and should not be the sole basis for threat hunting. We will continue to monitor PyPI and GitHub Advisory Database feeds for packages exhibiting these build hook and package content abuse signatures.

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Published via PulseTrace — Adverse Trace threat intelligence.