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| CVE | CVSS | EPSS | Vendor, product | Description | Modified |
|---|---|---|---|---|---|
| CVE-2021-34141 | 5.3 medium | 2.0% | numpy, oracle, communications_cloud_native_core_policy | An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. NOTE: the vendor states that this reported code behavior is "completely harmless." | 8 Oct |
| CVE-2026-14535 | 9.8 critical | 1.0% | trailofbits, fickling | In Trail of Bits fickling versions up to and including 0.1.11, the UnsafeImportsML analysis pass unconditionally calls AnalysisContext.shorten_code(node) on every import node it inspects, regardless of whether the import is flagged as unsafe. This call registers the shortened code representation in the shared AnalysisContext.reported_shortened_code set. When the MLAllowlist analysis pass subsequently runs, it calls the same shorten_code() method, receives already_reported=True for every import, and executes a continue statement that skips its allowlist check entirely. This renders MLAllowlist dead code for all imports — it never evaluates whether an import is in the ML allowlist or not. The MLAllowlist pass was designed to catch imports of modules outside the known-safe ML ecosystem (torch, numpy, transformers, etc.) that slip past the UnsafeImports denylist. With MLAllowlist inoperative, any standard library module not in the UNSAFE_IMPORTS denylist can be invoked via pickle deserialization while fickling's check_safety() returns LIKELY_SAFE. The fickling.load() API chains check_safety() into pickle.loads() as an explicit security gate, meaning a LIKELY_SAFE verdict causes the payload to be deserialized and executed. The root cause is shared mutable state between independently-correct analysis passes — UnsafeImportsML works as designed in isolation, MLAllowlist works as designed in isolation, but the shared reported_shortened_code set causes UnsafeImportsML to poison MLAllowlist's deduplication logic. | Sun 25 Oct |
| CVE-2026-79657 | 9.3 critical | 1.0% | nltk | NLTK versions before 3.10.3 contain a remote code execution vulnerability in allowlisted pickle loaders that trust entire module namespaces instead of specific safe callables. Attackers can craft malicious pickle payloads invoking dangerous in-namespace functions like ReppTokenizer._execute and numpy.f2py.crackfortran.myeval through pickle REDUCE to execute arbitrary commands during model or tokenizer artifact loading. | 8 Oct |
| CVE-2025-71355 | 7.6 high | 1.0% | no CPE yet | Picklescan before 0.0.25 fails to detect unsafe global functions in the Numpy library, allowing attackers to bypass static analysis and execute arbitrary code during deserialization. Attackers can craft malicious pickle files using numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries like os and execute arbitrary OS commands when the pickle file is loaded. | 12 Oct |
| CVE-2025-71347 | 7.6 high | 1.0% | no CPE yet | picklescan before 0.0.33 fails to detect malicious pickle files using numpy.f2py.crackfortran.param_eval function in reduce methods, allowing attackers to bypass security checks. Remote attackers can embed undetected code in pickle files that executes during deserialization, enabling arbitrary code execution in applications loading untrusted pickle data. | 12 Oct |
| CVE-2025-62608 | 5.5 medium | 1.0% | ml-explore, mlx | MLX is an array framework for machine learning on Apple silicon. Prior to version 0.29.4, there is a heap buffer overflow in mlx::core::load() when parsing malicious NumPy .npy files. Attacker-controlled file causes 13-byte out-of-bounds read, leading to crash or information disclosure. This issue has been patched in version 0.29.4. | 8 Oct |
| CVE-2025-71339 | 7.6 high | 0.00% | no CPE yet | Picklescan before 0.0.33 fails to detect the numpy.f2py.crackfortran._eval_length gadget in pickle __reduce__ methods, allowing arbitrary code execution. Attackers can craft malicious pickle files that execute arbitrary Python code when loaded by victims who trust Picklescan's safety validation. | 12 Oct |
| CVE-2025-71365 | 7.6 high | 0.00% | no CPE yet | picklescan before 0.0.33 fails to detect malicious pickle files that invoke numpy.f2py.crackfortran.myeval function through the reduce method. Attackers can craft malicious pickle files embedding arbitrary code that evades picklescan detection and executes remote code when loaded. | 12 Oct |
| CVE-2025-71362 | 7.6 high | 0.00% | no CPE yet | picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources. | 12 Oct |
| CVE-2025-71372 | 7.6 high | 0.00% | no CPE yet | Picklescan before 0.0.33 fails to detect the numpy.f2py.crackfortran.getlincoef gadget in pickle __reduce__ methods, allowing arbitrary code execution. Attackers can craft malicious pickle files that execute arbitrary Python code when loaded, bypassing Picklescan's safety checks and enabling supply-chain poisoning of shared model files. | 12 Oct |
| CVE-2026-115922 | 6.2 medium | 0.00% | no CPE yet | Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. Prior to 1.22.0, _unpack_2bit() and _unpack_4bit() resize unpacked arrays according to attacker-controlled TensorProto.dims without verifying that raw_data or int32_data encodes the declared number of elements. Materializing an untrusted UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor through onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator can therefore expand a small packed payload into a disproportionately large allocation and exhaust memory. This issue is fixed in version 1.22.0. | Mon 26 Oct |
| CVE-2026-102734 | 5.5 medium | 0.00% | no CPE yet | asteval is a Python expression evaluator designed for restricted execution. Prior to 1.0.9, in asteval/astutils.py, the default Interpreter with numpy enabled exposes a ctypes type and permits from_address memory access. safe_getattr blocks the ctypes module but not reachable ctypes type objects, while attribute assignments use unfiltered setattr instead of safe_setattr. An attacker-controlled expression can derive an address with id, read memory through from_address, and write through an attribute assignment. The expression can read or corrupt arbitrary process memory, escape the intended sandbox, execute code, disclose secrets, or crash the host process. This issue is fixed in version 1.0.9. | 16 Oct |
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EPSS scores: 2026-10-28. NVD cursor: 2026-10-29 00:22. The News page shows only notable CVEs (KEV, your stack, new criticals, EPSS over 50%, or active exploitation per SSVC); this table has all of them.