VAITP Dataset

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Total vulnerabilities in the dataset (not showing ignored and non-python related vulnerabilties): 1438
1597
CVE-2026-24688
pypdf: Crafted PDF outlines can trigger an infinite loop vulnerability.

pypdf is a free and open-source pure-python PDF library. An attacker who uses an infinite loop vulnerability that is present in versions prior to 6.6.2 can craft a PDF which leads to an infinite loop. This requires accessing the outlines/bookmarks. This has been fixed in pypdf 6.6.2. If projects cannot upgrade yet, consider applying the changes from PR #3610 manually.

Algorithm
Resource Management
Resource Exhaustion
Remote
1596
CVE-2026-24779
SSRF in vLLM's multimodal feature allows arbitrary internal requests.

vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.14.1, a Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods obtain and process media from URLs provided by users, using different Python parsing libraries when restricting the target host. These two parsing libraries have different interpretations of backslashes, which allows the host name restriction to be bypassed. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources. This vulnerability is particularly critical in containerized environments like `llm-d`, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state. Version 0.14.1 contains a patch for the issue.

Interface
Input Validation and Sanitization
Server-Side Request Forgery (SSRF)
Remote
1594
CVE-2026-24489
Gakido allows HTTP header injection via CRLF in header names and values.

Gakido is a Python HTTP client focused on browser impersonation and anti-bot evasion. A vulnerability was discovered in Gakido prior to version 0.1.1 that allowed HTTP header injection through CRLF (Carriage Return Line Feed) sequences in user-supplied header values and names. When making HTTP requests with user-controlled header values containing `\r\n` (CRLF), `\n` (LF), or `\x00` (null byte) characters, an attacker could inject arbitrary HTTP headers into the request. The fix in version 0.1.1 adds a `_sanitize_header()` function that strips `\r`, `\n`, and `\x00` characters from both header names and values before they are included in HTTP requests.

Checking
Input Validation and Sanitization
Command Injection
Remote
1593
CVE-2026-24486
Python-Multipart path traversal allows arbitrary file write via filename.

Python-Multipart is a streaming multipart parser for Python. Prior to version 0.0.22, a Path Traversal vulnerability exists when using non-default configuration options `UPLOAD_DIR` and `UPLOAD_KEEP_FILENAME=True`. An attacker can write uploaded files to arbitrary locations on the filesystem by crafting a malicious filename. Users should upgrade to version 0.0.22 to receive a patch or, as a workaround, avoid using `UPLOAD_KEEP_FILENAME=True` in project configurations.

Checking
Input Validation and Sanitization
Path Traversal
Remote
1592
CVE-2026-24408
CSRF in sigstore-python's OAuth flow due to missing state validation.

sigstore-python is a Python tool for generating and verifying Sigstore signatures. Prior to version 4.2.0, the sigstore-python OAuth authentication flow is susceptible to Cross-Site Request Forgery. `_OAuthSession` creates a unique "state" and sends it as a parameter in the authentication request but the "state" in the server response seems not not be cross-checked with this value. Version 4.2.0 contains a patch for the issue.

Checking
Authentication, Authorization, and Session Management
Cross-Site Request Forgery (CSRF)
Remote
1591
CVE-2026-24123
Path traversal in BentoML's bentofile.yaml exfiltrates files on build.

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to version 1.4.34, BentoML's `bentofile.yaml` configuration allows path traversal attacks through multiple file path fields (`description`, `docker.setup_script`, `docker.dockerfile_template`, `conda.environment_yml`). An attacker can craft a malicious bentofile that, when built by a victim, exfiltrates arbitrary files from the filesystem into the bento archive. This enables supply chain attacks where sensitive files (SSH keys, credentials, environment variables) are silently embedded in bentos and exposed when pushed to registries or deployed. Version 1.4.34 contains a patch for the issue.

Build/Package/Merge
Input Validation and Sanitization
Path Traversal
Local
1590
CVE-2026-0994
DoS in Protobuf Python via nested Any messages bypassing recursion limit.

A denial-of-service (DoS) vulnerability exists in google.protobuf.json_format.ParseDict() in Python, where the max_recursion_depth limit can be bypassed when parsing nested google.protobuf.Any messages. Due to missing recursion depth accounting inside the internal Any-handling logic, an attacker can supply deeply nested Any structures that bypass the intended recursion limit, eventually exhausting Pythonโ€™s recursion stack and causing a RecursionError.

Checking
Resource Management
Resource Exhaustion
Remote
1589
CVE-2026-0771
Code injection in Langflow Python functions allows remote code execution.

Langflow PythonFunction Code Injection Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Langflow. Attack vectors and exploitability will vary depending on the configuration of the product. The specific flaw exists within the handling of Python function components. Depending upon product configuration, an attacker may be able to introduce custom Python code into a workflow. An attacker can leverage this vulnerability to execute code in the context of the application. Was ZDI-CAN-27497.

Checking
Input Validation and Sanitization
Command Injection
Remote
1588
CVE-2026-0769
Langflow's eval_custom_component_code is vulnerable to unauthenticated RCE.

Langflow eval_custom_component_code Eval Injection Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Langflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the implementation of eval_custom_component_code function. The issue results from the lack of proper validation of a user-supplied string before using it to execute python code. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-26972.

Checking
Input Validation and Sanitization
Command Injection
Remote
1587
CVE-2026-0768
Langflow unauthenticated code injection allows remote execution as root.

Langflow code Code Injection Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Langflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of the code parameter provided to the validate endpoint. The issue results from the lack of proper validation of a user-supplied string before using it to execute Python code. An attacker can leverage this vulnerability to execute code in the context of root. . Was ZDI-CAN-27322.

Checking
Input Validation and Sanitization
Command Injection
Remote
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.

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