pypdf: Crafted PDF with self-referencing form causes memory exhaustion.
pypdf is a free and open-source pure-python PDF library. Prior to 6.12.2, an attacker who uses this vulnerability can craft a PDF which leads to large memory usage. This requires extracting the text of a page which contains a form XObject with self-references. This vulnerability is fixed in 6.12.2.
n8n Python Code node AST bypass can lead to environment variable disclosure.
n8n before 2.25.7 and 2.26.x before 2.26.2 contains an abstract syntax tree (AST) security validator bypass in the Python Code node. An authenticated user with permission to create or modify workflows containing a Python Code node can bypass the validator and access the task executor module namespace. The issue only affects self-hosted instances where the Python Task Runner is enabled; where N8N_BLOCK_RUNNER_ENV_ACCESS is configured to allow it, this can disclose environment variables accessible to the task runner process.
picklescan fails to detect profile.Profile.run, allowing code execution.
picklescan before 0.0.29 fails to detect the built-in python profile.Profile.run function when used in pickle reduce methods, allowing attackers to execute arbitrary code. Remote attackers can craft malicious pickle files that bypass picklescan detection and achieve code execution upon deserialization.
picklescan detection bypass via trace.Trace.runctx allows code execution.
picklescan before 0.0.29 fails to detect the built-in Python trace.Trace.runctx function when used in pickle file reduce methods, allowing attackers to execute arbitrary code. Remote attackers can craft malicious pickle files with trace.Trace.runctx payloads that bypass picklescan detection and execute code upon pickle.load() invocation.
Unauthenticated RCE in Orkes Conductor via malicious workflow definitions.
Orkes Conductor 3.21.21 before 3.30.2 contains an unauthenticated remote code execution vulnerability that allows remote attackers to execute arbitrary OS commands by submitting inline workflow definitions containing malicious JavaScript or Python expressions to the workflow API endpoint prior to authentication. Attackers can exploit unsandboxed GraalVM evaluators configured with HostAccess.ALL or allowAllAccess(true) through INLINE, LAMBDA, DO_WHILE, and SWITCH task types to invoke arbitrary system commands via Java reflection or direct subprocess calls.
RCE in LLaMA-Factory via malicious model path due to trust_remote_code=True.
LLaMA-Factory through 0.9.5 contains a remote code execution vulnerability that allows attackers with WebUI access to execute arbitrary Python code by supplying a malicious model path in the Chat or Training interfaces. The application passes user-supplied model path input unvalidated into AutoTokenizer.from_pretrained() and AutoModel.from_pretrained() with a hardcoded trust_remote_code=True parameter, causing the Hugging Face transformers library to fetch and execute arbitrary code from a remote or local model repository with the privileges of the server process.
NLTK 3.9.4 path traversal via percent-encoding allows arbitrary file read.
NLTK version 3.9.4 is vulnerable to a path traversal attack due to an incomplete fix for GitHub Issue #3504. The `_UNSAFE_NO_PROTOCOL_RE` regex in `nltk/data.py` checks for literal `../` sequences but fails to account for percent-encoded traversal sequences such as `..%2f`. The `url2pathname()` function decodes these sequences after the validation step, allowing an attacker to bypass the protection. This vulnerability enables an attacker to read arbitrary files accessible to the Python process by controlling the resource name parameter passed to `nltk.data.load()` or `nltk.data.find()`. The issue affects applications that rely on NLTK for resource loading, including NLP web applications, Jupyter notebooks, and CLI tools. The default `pathsec.ENFORCE=False` setting exacerbates the impact by not blocking the file read at the `open()` stage.
Snowflake CLI allows code execution via malicious Snowpark project content.
Improper neutralization in the Snowpark annotation processor callback template in Snowflake CLI versions prior to 3.19 allowed arbitrary code execution during application bundling or deployment. An attacker could exploit this by supplying crafted project content that is interpolated into generated Python code, causing Snowflake CLI to execute attacker-controlled code in the local context of the user running the CLI. Successful exploitation requires the victim to run the relevant bundling or deployment workflow against attacker-controlled project content, and any resulting code runs with the privileges of that local execution context. The fix is available in Snowflake CLI version 3.19, and users must manually upgrade.
Path traversal in Patool safe_extract allows writing files outside target.
Patool before 4.0.5 contains a path traversal vulnerability in the safe_extract() function in patoolib/programs/py_tarfile.py when running on Python before 3.12, where the is_within_directory() helper uses os.path.commonprefix() for character-level string comparison instead of path-level comparison, allowing a crafted archive member path to bypass the containment check. Attackers can supply a malicious archive with specially crafted member paths to write arbitrary files.
picklescan fails to detect code execution via idlelib in pickle files.
picklescan through 0.0.26 fails to detect malicious pickle files that invoke idlelib.pyshell.ModifiedInterpreter.runcode in __reduce__ methods. Attackers can embed undetected code in pickle files that executes arbitrary commands when the file is loaded via pickle.load(), enabling supply chain attacks on PyTorch models and saved Python objects. This is fixed in version 0.0.30.
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”
:: Shaping the future through research and ingenuity ::
