CVE Vulnerability Catalog

In nltk versions 3.9.3 and earlier, five Stanford interface classes (StanfordPOSTagger, StanfordNERTagger, StanfordParser, StanfordDependencyParser, and StanfordNeuralDependencyParser) are vulnerable to untrusted JAR code execution. These classes accept user-controllable JAR paths and execute them via the `java()` function, which invokes `subprocess.Popen()` without integrity verification. This vulnerability is identical to CVE-2026-0848, which was fixed for StanfordSegmenter by adding SHA256 verification, but the fix was not applied to these additional classes.

The Execute Command node in n8n allows authenticated users to execute arbitrary commands on the host system where n8n runs. Attackers with user access or compromised credentials can exploit this node to run malicious commands.

Picklescan before version 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().

The vulnerability in picklescan before version 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded.

Picklescan before version 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.

A vulnerability in picklescan before version 0.0.28 allows bypassing safety checks by using torch.utils.data.datapipes.utils.decoder.basichandlers in reduce methods. Attackers can embed undetected malicious code in pickle files that executes during deserialization.

The vulnerability in picklescan before version 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.

The vulnerability in picklescan before version 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 version 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.

The vulnerability in picklescan before version 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.

The vulnerability in picklescan before version 0.0.29 fails to detect malicious pickle files using the idlelib.calltip.get_entity function in reduce methods. Attackers can embed undetected code in pickle files that executes remote commands when loaded by victims.

A vulnerability in picklescan before version 0.0.29 allows bypassing detection of malicious pickle payloads that use the lib2to3.pgen2.grammar.Grammar.loads method in the reduce function. Attackers can craft pickle files embedding dangerous code that evades detection and executes during pickle.load() deserialization.

Picklescan before version 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.

A vulnerability in picklescan before version 0.0.28 allows bypassing detection of malicious pickle files. Attackers can exploit the torch._dynamo.guards.GuardBuilder.get function in reduce methods to embed code that evades scanning and executes arbitrary commands upon loading.

A vulnerability in picklescan before version 0.0.33 allows bypassing security checks by using the numpy.f2py.crackfortran.param_eval function in reduce methods. Attackers can embed undetected code in pickle files that executes during deserialization.

Picklescan before version 0.0.30 fails to detect malicious pickle files that invoke the torch.utils.bottleneck.__main__.run_autograd_prof function. Attackers can embed undetected code in pickle files that executes during deserialization, enabling remote code execution.

A vulnerability in picklescan before version 0.0.30 allows bypassing detection of malicious pickle files exploiting the lib2to3.pgen2.pgen.ParserGenerator.make_label function in the reduce method. Attackers can craft pickle files with embedded code that evades detection but executes arbitrary commands when pickle.load() is called.

The vulnerability in picklescan before version 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.

An Incorrect Use of Privileged APIs vulnerability in Unity Parsec on Windows hosts leads to a potential Elevation of Privilege. This issue affects Parsec through v2026-05-04.0. The patched version is Parsec for Windows version 150-104a. A user can generate a situation where there is an instance of parsecd.exe running as NT AUTHORITY\SYSTEM with a user-controlled value of the AppData environment variable.

An improper access control vulnerability in Microsoft Edge for Android allows an unauthorized attacker to bypass a security feature over a network. The issue stems from inadequate access controls within the browser.