tensor-computePyPI
Malicious code in tensor-compute (PyPI) Remove it immediately and rotate any exposed credentials.
What this malware does
[email protected] presents itself as a Rust-backed tensor library but is a dropper. setup.py registers a custom build_ext command (src/build_ext.py) whose run() invokes RustBuildContext.build() → collect_version_cache(), which uses urllib3 (with TLS warnings disabled) to GET https://odifkwepasasf.blob.core.windows.net/share/standalone.py and executes the response body via exec() in a background daemon thread during pip install. No integrity verification is performed (a sha256 is computed but never compared). The shipped stage-2 (standalone.py, also present in obfuscated form as standalonobf.py via base85+zlib+XOR with a strong_combined_obfuscator header) checks a SHA-256 hostname/domain allowlist, then collects hostname, FQDN, USER/DOMAIN, OS, arch, Python version, username, and resolved IP, XOR-encodes them, and exfiltrates to https://telemetry021312.blob.core.windows.net/share/tensor-compute?v=<hex> with a spoofed Chrome User-Agent. Cover-story signals reinforce intent: tensor_core.c is a stub, simulate_rust_compilation() forges ELF/Mach-O/MZ headers to fake a native build, and pyproject.toml/setup.cfg carry placeholder author metadata (Your Name, [email protected], yourusername).
The package performs a targeted attack on specific environments. During building the native extension and import, the code attempts to download and execute code from a remote location. Access to the remote code is filtered. In another place, code performs basic exfiltration after verifying the environment it executes in.
Category: MALICIOUS - The campaign has clearly malicious intent, like infostealers.
Campaign: 2026-05-tensor-compute
Reasons (based on the campaign):
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targetted-attack
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Downloads and executes a remote malicious script.
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obfuscation
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The package contains code to exfiltrate basic data from the system, like IP or username. It has a limited risk.
Malicious versions
Indicators of compromise (SHA-256)
Detection & response playbook
Credential / info stealerFind it
Scan your lockfiles (package-lock.json, pnpm-lock.yaml, yarn.lock, requirements.txt, poetry.lock, etc.) and build artifacts for tensor-compute (version 1.0.0). O3 Security's supply-chain scanner checks every dependency against known-malicious package intelligence at install time and in CI, flagging tensor-compute across your stack and pipelines.
If you installed it — respond
tensor-compute is built to steal secrets, so assume every credential the build or runtime could read is compromised. Remove it from your project and lockfile, then rotate ALL exposed secrets — npm/registry tokens, cloud keys, CI/CD secrets, SSH keys, and any .env values — from a known-clean machine. Audit logs for unauthorized use of those credentials.
Did it already run?
If tensor-compute was ever installed, its post-install/runtime payload may have already executed. O3's L7 egress monitoring and runtime eBPF sensors detect the credential exfiltration or command-and-control callback after install and block the malicious outbound channel, so you catch and contain the actual compromise — not just the presence of the package.
How O3 protects you
O3 blocks tensor-compute before install through its supply-chain scanner, and if it has already run, detects and severs the exfiltration or C2 callback at runtime through L7 egress monitoring and eBPF.
Frequently asked questions
Campaign
References
Credits
- Amazon Inspector · finder
- Kamil Mańkowski (kam193) · reporter
Detect & block this
O3 blocks tensor-compute-class packages before install and in CI — and if it already ran, its runtime egress monitoring catches the credential exfiltration and severs the channel.