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A vulnerability in the Python PLY (Python Lex-Yacc) library allows attackers to execute arbitrary code on vulnerable systems, raising concerns for applications that rely on cached parser tables.
The flaw, which affects PLY version 3.11 distributed via PyPI, has a publicly available proof-of-concept (PoC) and enables remote code execution during application startup.
This vulnerability allows attackers “… arbitrary code execution, execution during application startup, and code execution before any parsing logic is reached,” said researchers in the advisory.
Inside the PLY Pickle Deserialization Flaw
PLY is widely embedded in Python applications that implement custom parsers, including compilers, configuration engines, and domain-specific languages.
In many of these systems, parser initialization occurs early in the application lifecycle and is implicitly trusted.
As a result, vulnerabilities at this layer can have outsized impact, potentially leading to full system compromise before most security controls are even active.
The issue, tracked as CVE-2025-56005, carries a high-risk profile because exploitation does not rely on malicious input being parsed.
Instead, the vulnerable code executes before parsing logic begins, rendering traditional input validation, sandboxing, and runtime monitoring largely ineffective.
At the center of the vulnerability is an undocumented picklefile parameter in the yacc() function in PLY version 3.11.
When this parameter is set, PLY attempts to load cached parser tables from disk using Python’s pickle.load() function — without performing any integrity checks, validation, or origin verification on the file being deserialized.
This behavior is dangerous because Python’s pickle module is inherently unsafe for untrusted data.
During deserialization, objects can define a __reduce__() method that executes arbitrary code.
As a result, loading a malicious pickle file guarantees code execution, often occurring before application logging, security instrumentation, or privilege restrictions are fully initialized.
In practical terms, an attacker who can influence the path or contents of the pickle file can execute arbitrary system commands simply by triggering parser initialization.
An available proof-of-concept demonstrates that when yacc(picklefile=”exploit.pkl”) loads a crafted pickle containing a malicious __reduce__() payload, code execution happens immediately — without user interaction or parsing input.
Exploitation
Exploitation becomes feasible in environments where parser table files can be controlled, replaced, or otherwise poisoned.
Some example exploitation scenarios include cached parser table directories stored on disk, shared network file systems accessed by multiple services, CI/CD pipeline artifacts that are reused across builds, and application-defined file paths that are writable or configurable.
Because these resources are often treated as trusted internal components, they may lack proper monitoring or integrity controls, increasing the likelihood of unnoticed compromise.
Reducing Risk From Startup-Time Code Execution
Because this vulnerability enables code execution during application startup, organizations should focus on preventing unsafe deserialization and limiting trust in parser-related artifacts.
Simply validating runtime inputs is insufficient when exploitation occurs before normal security controls are active.
A defensive approach that combines code review, filesystem hardening, and build pipeline protections is important for helping to reduce risk.
- Audit applications for use of the undocumented picklefile parameter and avoid loading parser tables from disk where possible.
- Treat all pickle files as untrusted input and eliminate unsafe deserialization paths during parser initialization.
- Restrict parser cache locations to non-writable directories and apply strict filesystem permissions.
- Harden CI/CD pipelines to prevent artifact poisoning and unauthorized modification of build outputs.
- Run parser initialization in isolated or least-privileged execution environments to limit impact if exploitation occurs.
- Monitor critical file paths and startup behavior for unexpected file changes or process execution.
- Integrate unsafe deserialization scenarios into security operations and regularly test incident response plans to account for startup-time code execution.
These measures help contain the blast radius of exploitation and build resilience against startup-time compromises that can otherwise bypass traditional security controls.
Build Artifacts as an Attack Vector
This vulnerability highlights the risks posed by unsafe deserialization and implicit trust in internal application components, particularly during early execution stages where security controls are limited.
As libraries like PLY remain embedded in critical parsing and build workflows, organizations should reassess how startup-time code paths are secured and monitored.
Reducing reliance on unvalidated artifacts, tightening filesystem and pipeline controls, and isolating high-risk initialization logic can help limit the impact of exploitation.
Managing these risks fits naturally within zero-trust frameworks that continuously validate components and execution contexts.
