Summary
A remote code execution vulnerability in MONAI's algo_from_pickle() function allows malicious pickle data to execute arbitrary system commands during deserialization. The issue becomes risky when an attacker can supply or influence a pickle file that the vulnerable function processes.
Affected context
The vulnerability occurs in MONAI Auto3DSeg workflows that use algo_from_pickle() to deserialize pickle files through pickle.loads(). Exploitation depends on an untrusted or attacker-controlled pickle file reaching this deserialization path; trusted, integrity-protected inputs reduce exposure but do not eliminate the underlying insecure pattern.
Exploit preconditions
- Attack surface: Code paths that invoke
monai/auto3dseg/utils.pythroughalgo_from_pickle() - Required access: Ability to create, replace, upload, or otherwise influence a pickle file processed by the application
- Trigger point: Deserialization of the attacker-influenced pickle data
- Why exploitation is possible: Python pickle deserialization can invoke object behaviors that execute code rather than merely reconstructing passive data structures
Why this happens
The underlying defect is the use of Python's general-purpose pickle deserialization on data that may not be fully trusted. Pickle can encode executable object reconstruction behavior, so deserializing attacker-controlled content may invoke arbitrary system operations. This maps to CWE-502: Deserialization of Untrusted Data, because serialized input is processed as trusted executable object state.
Potential impact
- An attacker may execute arbitrary operating system commands with the application's privileges.
- The compromised process could expose, alter, or destroy local data.
- Credentials, tokens, or model artifacts could be accessed.
- The affected host or workload could be used for lateral movement or further compromise.
General mitigation guidance
Mitigation generally focuses on removing unsafe pickle deserialization from untrusted data paths and reducing the trust placed in serialized artifacts.
Common defensive patterns include:
- Replacing pickle with a non-executable, data-only serialization format where feasible.
- Applying strict integrity checks, provenance controls, and allowlisted sources to required serialized artifacts.
- Validating file type, structure, and expected content before processing.
- Isolating model and algorithm processing in least-privileged workloads with restricted filesystem and network access.
- Avoiding execution under identities with unnecessary operating-system or cloud permissions.
- Treating downloaded, uploaded, and shared artifacts as untrusted by default.
How teams detect issues like this
Detection programs commonly combine:
- Static analysis rules for
pickle.loads(),pickle.load(), and related deserialization APIs on externally influenced data. - Code review checks for model or algorithm files accepted from uploads, remote repositories, shared storage, or user-controlled paths.
- Dependency and policy scans that flag unsafe serialization patterns in machine-learning pipelines.
- Runtime monitoring for unexpected child processes, shell activity, filesystem changes, or network connections during artifact loading.
- CI enforcement requiring approved serialization formats and documented artifact provenance.
Automated code-review and software-composition tools may help identify these patterns, but findings should be assessed against the actual data flow and deployment controls.
