Informational scan, not a security audit. How this is computed.
API keys, passwords or tokens committed into the repo.
This check didn’t finish — that’s not the same as “clean.” Try Check again above.
Packages you depend on that have known security holes (CVEs).
CVE-2026-25990 pillow: Pillow: Out-of-bounds Write via Specially Crafted PSD ImageCVE-2026-40192 Pillow: Pillow: Denial of Service via decompression bomb in FITS image processingCVE-2026-42311 Pillow: python-pillow: Pillow: Arbitrary code execution via malicious PSD file processingCVE-2026-54058 Pillow: Pillow: Memory disclosure or denial of service via crafted McIdas AREA imageCVE-2026-54059 python-pillow: Pillow: Denial of Service via crafted PCF font dataCVE-2026-54060 python-pillow: Pillow: Denial of Service via excessive memory allocation when processing font filesCVE-2026-55379 python-pillow: Pillow: Denial of Service via crafted BDF font fileCVE-2026-55380 python-pillow: Pillow: Denial of Service via crafted GD 2.x image fileCVE-2026-59197 Pillow: Pillow: Native heap out-of-bounds writeCVE-2026-59199 Pillow: Pillow: Denial of Service via out-of-bounds write in image processingCVE-2026-59200 Pillow: Pillow: Denial of service via crafted PDF streamCVE-2026-59204 Pillow: Pillow: Denial of Service via crafted JPEG2000 imageCVE-2026-59205 Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply APICVE-2026-42308 Pillow: python: Pillow: Denial of Service via integer overflow in font processingCVE-2026-42310 Pillow: Pillow: Denial of Service via malicious PDF processingCVE-2026-55798 python-pillow: Pillow: Arbitrary command injection via shell metacharacters in file pathsCVE-2026-59198 Pillow: Pillow: Information disclosure via TGA RLE encoder out-of-bounds readCVE-2026-40171 Jupyter Notebook: JupyterLab: @jupyter-notebook/help-extension: @jupyterlab/help-extension: Jupyter Notebook and JupyterLab: Session takeover via stored cross-site scriptingCVE-2026-42557 jupyterlab: JupyterLab: Arbitrary code execution via deceptive button in HTML outputCVE-2025-71176 pytest: pytest: Denial of Service or Privilege Escalation via insecure temporary directory handlingCVE-2022-40897 pypa-setuptools: Regular Expression Denial of Service (ReDoS) in package_index.pyCVE-2024-6345 pypa/setuptools: Remote code execution via download functions in the package_index module in pypa/setuptoolsCVE-2025-47273 setuptools: Path Traversal Vulnerability in setuptools PackageIndexCVE-2026-59890 setuptools: setuptools: MANIFEST.in exclusion bypass in sdist via Unicode normalization collision (NFC/NFD)CVE-2025-47273 setuptools: Path Traversal Vulnerability in setuptools PackageIndexYour dependencies cross-checked against the OSV vulnerability database.
PYSEC-2025-41 PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command EPYSEC-2026-2681 JupyterLab's command linker attributes in HTML enable one-click command execution from untrusted contentPYSEC-2026-1471 Jinja2 vulnerable to sandbox breakout through attr filter selecting format methodPYSEC-2026-1472 Jinja has a sandbox breakout through malicious filenamesPYSEC-2026-1475 Jinja has a sandbox breakout through indirect reference to format methodPYSEC-2026-1374 filelock Time-of-Check-Time-of-Use (TOCTOU) Symlink Vulnerability in SoftFileLockPYSEC-2026-1375 filelock has a TOCTOU race condition which allows symlink attacks during lock file creationPYSEC-2026-1805 protobuf affected by a JSON recursion depth bypassPYSEC-2026-1806 protobuf-python has a potential Denial of Service issuePYSEC-2025-191 A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch.mkldnn_max_pool2d. The manipulation leads to denial of servicePYSEC-2025-198 In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.PYSEC-2025-203 An issue in the component torch.linalg.lu of pytorch v2.8.0 allows attackers to cause a Denial of Service (DoS) when performing a slice operation.PYSEC-2025-204 pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.PYSEC-2025-205 A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).PYSEC-2025-206 pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().PYSEC-2025-207 A Name Error occurs in pytorch v2.7.0 when a PyTorch model consists of torch.cummin and is compiled by Inductor, leading to a Denial of Service (DoS).PYSEC-2025-208 A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv() and is compiled by Inductor, leading to a PYSEC-2025-209 An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor.PYSEC-2026-139 A vulnerability was identified in PyTorch 2.10.0. The affected element is an unknown function of the component pt2 Loading Handler. The manipulation leads to deserialization. The attack can only be pePYSEC-2026-1970 PyTorch Improper Resource Shutdown or Release vulnerabilityPYSEC-2026-2286 PyTorch is a Python package that provides tensor computation. Prior to version 2.10.0, a vulnerability in PyTorch's `weights_only` unpickler allows an attacker to craft a malicious checkpoint file (`.GHSA-c678-jfcj-6jmf PyTorch Tuple Handler is Vulnerable to Memory Corruption through Manipulation of None ArgumentGHSA-f4hp-rmr7-r7v8 PyTorch is Vulnerable to Memory Consumption through pad_packed_sequence FunctionGHSA-qfhq-4f3w-5fph PyTorch is vulnerable to memory corruption through its torch.lstm_cell functionGHSA-rrmf-rvhw-rf47 PyTorch is vulnerable to memory corruption through its torch.jit.script functionCode that can be exploited: injection, hardcoded credentials and similar.
Nothing found by this check. ✓
Packages that look intentionally malicious: typosquats, sneaky install scripts.
This check didn’t finish — that’s not the same as “clean.” Try Check again above.
A signal about how the project is maintained — not a vulnerability in your code. It doesn’t affect the verdict above.
Maintenance & supply-chain hygiene. A signal about the project, not a vulnerability in your code.
This check didn’t finish — that’s not the same as “clean.” Try Check again above.