Deserialization of Untrusted Data

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.Show less

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.Show less

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.Show less

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.Show less

picklescan before 0.0.29 fails to detect malicious pickle payloads that utilize lib2to3.pgen2.grammar.Grammar.loads in the reduce method, allowing remote code execution. Attackers can craft pickle files embedding dangerous code that evades picklescan detection and executes during pickle.load() deserialization.Show less

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.Show less

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.Show less

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.Show less

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

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.Show less

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.Show less

A vulnerability in keras-team/keras version 3.14.0 allows for arbitrary code execution due to improper handling of deserialization in the `Lambda` layer. Specifically, the `_raise_for_lambda_deserialization()` function fails to enforce the safe-mode guard when `safe_mode` is set to `None`, which is the default value when `from_config()` is called outside of a `SafeModeScope` context. This logic error conflates `None` (unset/default-deny) with `False` (explicitly disabled), bypassing the guard and allowing attacker-controlled `marshal` bytecode to be deserialized. Affected call sites include `keras.layers.deserialize(config)`, `keras.models.clone_model(model)`, and any direct invocation of `Lambda.from_config(config)` without an enclosing `SafeModeScope(True)`. This vulnerability can be exploited to achieve arbitrary OS-level code execution in the context of the server or user process.Show less

An authenticated administrator can trigger a denial-of-service condition in the Fireware Management Web UI by sending malformed or crafted data to the put_data endpoint, which performs unsafe deserialization of the attacker-supplied input.Show less

mchange-commons-java is a Java library of shared utility classes used by mchange projects like the c3p0 connection pool. Prior to version 0.6.0, its JNDI ObjectFactory implementation (com.mchange.v2.naming.JavaBeanObjectFactory) will construct objects of arbitrary classes and initialize "JavaBean"-style properties, which for certain classes enables JNDI injection and "deserialization gadgets." Such initialization is unsafe for some classes: for example, setting the contentType property of a Swing JEditorPane to text/html and its text property to HTML containing a stylesheet <link> will provoke an HTTP GET on an arbitrary URL, potentially from within a trusted security domain. The problem is aggravated by the library's ReferenceIndirector, through which malicious JNDI Reference objects can be smuggled in for dereferencing wherever an application reads a Java-serialized object. This has been resolved in version 0.6.0.Show less

Deserialization of untrusted data in the RemoteQueryCachePlugin in Amazon Web Services AWS Advanced JDBC Wrapper 3.3.0 through 4.0.0 might allow an actor with write access to the shared cache infrastructure to execute arbitrary code on application servers that read cached query results via a crafted serialized Java object. The RemoteQueryCachePlugin uses ObjectInputStream without class filtering when deserializing cached query results from Redis or Valkey, enabling gadget chain execution when cache entries are poisoned. We recommend upgrading to AWS Advanced JDBC Wrapper version 4.0.1 or later.Show less