Fickling is vulnerable to detection bypass due to "builtins" blindness.
Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, Fickling is vulnerable to detection bypass due to "builtins" blindness. This issue has been patched in version 0.1.7.
Fickling fails to detect unsafe imports, allowing arbitrary code execution.
Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, the unsafe_imports() method in Fickling's static analyzer fails to flag several high-risk Python modules that can be used for arbitrary code execution. Malicious pickles importing these modules will not be detected as unsafe, allowing attackers to bypass Fickling's primary static safety checks. This issue has been patched in version 0.1.7.
Fickling scanner bypass via pydoc/ctypes allows remote code execution.
Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, both ctypes and pydoc modules aren't explicitly blocked. Even other existing pickle scanning tools (like picklescan) do not block pydoc.locate. Chaining these two together can achieve RCE while the scanner still reports the file as LIKELY_SAFE. This issue has been patched in version 0.1.7.
Fickling misclassifies cProfile pickles, allowing remote code execution.
Fickling is a Python pickling decompiler and static analyzer. Fickling versions up to and including 0.1.6 do not treat Python's cProfile module as unsafe. Because of this, a malicious pickle that uses cProfile.run() is classified as SUSPICIOUS instead of OVERTLY_MALICIOUS. If a user relies on Fickling's output to decide whether a pickle is safe to deserialize, this misclassification can lead them to execute attacker-controlled code on their system. This affects any workflow or product that uses Fickling as a security gate for pickle deserialization. This issue has been patched in version 0.1.7.
Fickling misclassifies pickles using `runpy`, leading to code execution.
Fickling is a Python pickling decompiler and static analyzer. Fickling versions up to and including 0.1.6 do not treat Python’s runpy module as unsafe. Because of this, a malicious pickle that uses runpy.run_path() or runpy.run_module() is classified as SUSPICIOUS instead of OVERTLY_MALICIOUS. If a user relies on Fickling’s output to decide whether a pickle is safe to deserialize, this misclassification can lead them to execute attacker-controlled code on their system. This affects any workflow or product that uses Fickling as a security gate for pickle deserialization. This issue has been patched in version 0.1.7.
CSRF in Authlib due to cache state not being tied to the user session.
Authlib is a Python library which builds OAuth and OpenID Connect servers. In version 1.6.5 and prior, cache-backed state/request-token storage is not tied to the initiating user session, so CSRF is possible for any attacker that has a valid state (easily obtainable via an attacker-initiated authentication flow). When a cache is supplied to the OAuth client registry, FrameworkIntegration.set_state_data writes the entire state blob under _state_{app}_{state}, and get_state_data ignores the caller’s session altogether. This issue has been patched in version 1.6.6.
Improper type conversion in Logging Redactor can cause log formatting errors.
Logging Redactor is a Python library designed to redact sensitive data in logs based on regex patterns and / or dictionary keys. Prior to version 0.0.6, non-string types are converted into string types, leading to type errors in %d conversions. The problem has been patched in version 0.0.6. No known workarounds are available.
SQL Injection in parsl-visualize via URL allows for data exfiltration.
Parsl is a Python parallel scripting library. A SQL Injection vulnerability exists in the parsl-visualize component of versions prior to 2026.01.05. The application constructs SQL queries using unsafe string formatting (Python % operator) with user-supplied input (workflow_id) directly from URL routes. This allows an unauthenticated attacker with access to the visualization dashboard to inject arbitrary SQL commands, potentially leading to data exfiltration or denial of service against the monitoring database. Version 2026.01.05 fixes the issue.
A Redis connection leak in NiceGUI allows for unauthenticated DoS.
NiceGUI is a Python-based UI framework. From versions v2.10.0 to 3.4.1, an unauthenticated attacker can exhaust Redis connections by repeatedly opening and closing browser tabs on any NiceGUI application using Redis-backed storage. Connections are never released, leading to service degradation when Redis hits its connection limit. NiceGUI continues accepting new connections - errors are logged but the app stays up with broken storage functionality. This issue has been patched in version 3.5.0.
NiceGUI allows cross-site URL fragment manipulation via an iframe.
NiceGUI is a Python-based UI framework. From versions 2.22.0 to 3.4.1, an unsafe implementation in the pushstate event listener used by ui.sub_pages allows an attacker to manipulate the fragment identifier of the URL, which they can do despite being cross-site, using an iframe. This issue has been patched in version 3.5.0.
Introducing the "VAITP dataset": a specialized repository of Python vulnerabilities and patches, meticulously compiled for the use of the security research community. As Python's prominence grows, understanding and addressing potential security vulnerabilities become crucial. Crafted by and for the cybersecurity community, this dataset offers a valuable resource for researchers, analysts, and developers to analyze and mitigate the security risks associated with Python. Through the comprehensive exploration of vulnerabilities and corresponding patches, the VAITP dataset fosters a safer and more resilient Python ecosystem, encouraging collaborative advancements in programming security.
The supreme art of war is to subdue the enemy without fighting.
Sun Tzu – “The Art of War”
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