| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure. |
| NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause improper validation of allowed inputs. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure. |
| An issue in Pivotal CRM 6.6.4.08 and systems using patch-ghi-15381-cwe-502-20251225.zip (fixed in Pivotal CRM 6.6.5.10 and Patch_CWE502_20260316.zip) allows a remote attacker to execute arbitrary code via the Pivotal.Engine.Client.Services.Conversion.dll component. NOTE: this issue exists because of an incomplete fix for CVE-2026-39253. |
| NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure. |
| NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure. |
| NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure. |
| NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure. |
| NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause improper control of dynamically managed code resources. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure. |
| picklescan before 0.0.28 fails to detect malicious pickle files that use torch.utils.data.datapipes.utils.decoder.basichandlers in reduce methods, allowing attackers to bypass safety checks. Remote attackers can embed undetected malicious code in pickle files that executes during deserialization, enabling remote code execution. |
| 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. |
| picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks. |
| picklescan before 0.0.29 fails to detect malicious pickle files using idlelib.calltip.get_entity function in reduce methods. Attackers can embed undetected code in pickle files that executes remote commands when loaded by victims. |
| picklescan before 0.0.34 fails to detect _operator.attrgetter function calls in pickle payloads, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle files using _operator.attrgetter in reduce methods to execute arbitrary code when pickle.load() processes the file. |
| picklescan before 0.0.28 fails to detect malicious pickle files that exploit torch._dynamo.guards.GuardBuilder.get function in reduce methods. Attackers can craft pickle files with embedded code that evades picklescan detection and executes arbitrary commands when loaded. |
| picklescan before 0.0.30 fails to detect malicious pickle files that exploit lib2to3.pgen2.pgen.ParserGenerator.make_label function in the reduce method. Attackers can craft malicious pickle files with embedded code that evades detection but executes arbitrary commands when pickle.load() is called. |
| picklescan before 0.0.28 fails to detect malicious torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression function calls in pickle files. Attackers can embed undetected code in pickle files that executes remote code when loaded by victims. |
| 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. |
| picklescan before 0.0.30 fails to detect the asyncio.unix_events._UnixSubprocessTransport._start function in pickle reduce methods, allowing remote code execution. Attackers can craft malicious pickle files embedding this built-in function that evade detection but execute arbitrary commands when loaded. |
| picklescan before 0.0.28 fails to detect malicious torch.utils.bottleneck.__main__.run_cprofile function calls in pickle files, allowing attackers to bypass safety checks. Remote attackers can embed undetected code in pickle files to achieve arbitrary code execution when victims load the files. |
| picklescan before 0.0.34 fails to detect the _operator.methodcaller built-in function when scanning pickle files for malicious code. Attackers can craft malicious pickle payloads using _operator.methodcaller that evade detection and execute arbitrary code when loaded by pickle.load(). |
