CVE-2026-14535: fickling: shared-state bug disables ML pickle allowlist check

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.

An attacker crafts a malicious PyTorch/pickle model file that imports a standard library module absent from fickling's UNSAFE_IMPORTS denylist (rather than an obviously dangerous module like os.system, which would be blocked) and embeds a __reduce__ payload that invokes that module's functionality to achieve code execution on load. The attacker uploads this file to a public model-sharing platform, an internal model registry with an open-upload workflow, or sends it via email/shared drive to a data science team member with a lure ('updated fine-tuned checkpoint'). The victim organization's pipeline runs fickling.load() as its security gate; because UnsafeImportsML has already marked the import as 'reported' via shorten_code(), MLAllowlist silently no-ops and check_safety() returns LIKELY_SAFE, so the file proceeds straight into pickle.loads() and executes the attacker's code with the privileges of the ML pipeline or the data scientist's workstation — potentially pivoting further into training infrastructure, cloud credentials, or the model registry itself.