VAITP Dataset

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Total vulnerabilities in the dataset (not showing ignored and non-python related vulnerabilties): 1787
1944
pyLoad authenticated SSRF via redirect allows internal network access.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev100, the PREREQFUNCTION-based private IP check was not applied to HTTPRequest (used by the parse_urls API). An authenticated attacker can supply a URL pointing to an attacker-controlled server that responds with a 302 redirect to an internal/private IP address, bypassing the is_global_host() check on the initial URL. This vulnerability is fixed in 0.5.0b3.dev100.

Checking
Input Validation and Sanitization
Server-Side Request Forgery (SSRF)
Remote
1942
pyLoad allows authenticated users to steal session files for account takeover.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev100, the fix for CVE-2026-33509 prevents setting storage_folder inside PKGDIR or userdir, but does NOT protect the Flask session directory (/tmp/pyLoad/flask). An authenticated attacker can set storage_folder to the session directory and download session files of other users via /files/get/, leading to account takeover. This vulnerability is fixed in 0.5.0b3.dev100.

Checking
Input Validation and Sanitization
Path Traversal
Remote
1941
OpenReplay API allows cross-tenant access to session data via API keys.

OpenReplay is a self-hosted session replay suite. Prior to 1.26.0, OpenReplay's Python API exposes several app_apikey routes that trust a caller-provided projectKey after validating only that the API key itself is valid and that the target projectKey exists. The authorization flow does not verify that the authenticated API key and the requested project belong to the same tenant. Because the public tracker design exposes projectKey to browser-side code, an attacker who owns any valid API key for their own tenant can target another tenant's project by reusing that public projectKey. The vulnerable routes allow the attacker to enumerate victim user sessions and then retrieve sensitive session event data across the tenant boundary. This vulnerability is fixed in 1.26.0.

Checking
Authentication, Authorization, and Session Management
Insecure Direct Object References (IDOR)
Remote
1940
PyJWT allows using a public key as an HMAC secret, enabling signature forgery.

PyJWT is a JSON Web Token implementation in Python. Prior to 2.13.0, when the verifier is decoding JSON Web Tokens, while supporting both asymmetric and HMAC algorithms, the library does not validate use of JSON Web Keys in HMAC algorithm, allowing attacker to use the issuer public key as the secret key for HMAC algorithm. This vulnerability is fixed in 2.13.0.

Checking
Cryptographic
Cryptographic Implementation Error
Remote
1939
PyJWT allows unauthenticated DoS via crafted detached JWS payloads.

PyJWT is a JSON Web Token implementation in Python. From 2.8.0 to 2.12.1, when verifying detached JWS tokens using the unencoded-payload option ("b64": false, RFC 7797), PyJWT performs Base64URL decoding of the compact-serialization payload segment before enforcing the detached-payload rules. For b64=false, PyJWT later discards that decoded payload and replaces it with the caller-provided detached_payload. In practice, this turns the middle segment into an attacker-controlled โ€œwork amplifierโ€: a remote client can supply an arbitrarily large Base64URL payload segment that forces CPU work + memory allocations even if the signature is invalid. This creates an unauthenticated DoS vector against any endpoint that verifies detached JWS using PyJWT. This vulnerability is fixed in 2.13.0.

Timing/Serialization
Resource Management
Resource Exhaustion
Remote
1938
PyJWT allows DoS via unlimited JWKS requests from an unverified JWT `kid`.

PyJWT is a JSON Web Token implementation in Python. Prior to 2.13.0, PyJWKClient.get_signing_key() forces a fresh HTTP request to the JWKS endpoint for every JWT with an unknown kid value, with no rate limiting. Since kid comes from the unverified token header, an attacker can trigger unlimited outbound requests. The vulnerability surfaces only when a JWKS fetch fails; an attacker can attempt to provoke that with sustained unknown-kid traffic, but the outcome depends on upstream JWKS-endpoint behavior (rate limiting, transient errors) which is beyond the attacker's control. This vulnerability is fixed in 2.13.0.

Checking
Resource Management
Server-Side Request Forgery (SSRF)
Remote
1937
PyJWT allows an algorithm allow-list bypass when verifying with a PyJWK key.

PyJWT is a JSON Web Token implementation in Python. From 2.9.0 to 2.12.1, there is a verifier-side algorithm allow-list bypass when jwt.decode() or jwt.decode_complete() are called with a PyJWK key. The token header alg is checked against the caller-supplied algorithms allow-list, but signature verification is performed with the algorithm bound to the PyJWK object instead of the header algorithm. An attacker who controls a registered JWK/JWKS private key can sign with a disallowed algorithm, advertise an allowed algorithm in the JWT header, and still be accepted. The issue affects the documented PyJWKClient.get_signing_key_from_jwt(...) flow. This vulnerability is fixed in 2.13.0.

Checking
Cryptographic
Cryptographic Implementation Error
Remote
1936
PyJWT's PyJWKClient is vulnerable to SSRF via non-HTTP(S) URI schemes.

PyJWT is a JSON Web Token implementation in Python. Prior to 2.13.0, PyJWKClient passes its uri argument directly to urllib.request.urlopen() which uses Python stdlib's default OpenerDirector registering HTTPHandler, HTTPSHandler, FTPHandler, FileHandler, and DataHandler. There is currently no documented option to restrict which schemes PyJWKClient will fetch. If an application's jku URL ingestion path accepts attacker-influenced URLs (e.g., from JWT header, configuration file, OAuth flow parameter), the attacker can cause PyJWKClient to read arbitrary local files via file:// (SSRF on local filesystem), cause PyJWKClient to attempt FTP / data-URI fetches (broader SSRF surface), or forge tokens that PyJWT verifies as valid. The library does not directly return non-HTTP(S) URI contents to the attacker; the chained "plant a JWKS to forge tokens" scenario described in the original report requires additional application-layer flaws (attacker write access to a filesystem path, untrusted jku derivation) that this fix does not address. This vulnerability is fixed in 2.13.0.

Checking
Input Validation and Sanitization
Server-Side Request Forgery (SSRF)
Remote
1935
Python Liquid path traversal allows reading arbitrary files via include tags.

Python Liquid is a Python engine for the Liquid template language. Prior to 2.2.0, the built-in FileSystemLoader and CachingFileSystemLoader do not guard against reading files outside their search paths when given an absolute path to resolve. This allows malicious template authors to load and render arbitrary files via the {% include %} and {% render %} tags. Targeted files would need to contain valid Liquid markup and be readable by the application process. This vulnerability is fixed in 2.2.0.

Checking
Input Validation and Sanitization
Path Traversal
Remote
1934
Code injection in quota-statusline.sh via a crafted hook stdin payload.

claude-code-cache-fix is a cache optimization proxy for Claude Code. From 3.5.0 to before 3.5.2, tools/quota-statusline.sh (introduced in v3.5.0) interpolates Claude Code's hook stdin payload directly into a Python triple-quoted string literal. A ''' byte sequence in any user-controlled field of the payload closes the literal early and lets following bytes execute as Python in the user's Claude Code process. This vulnerability is fixed in 3.5.2.

Checking
Input Validation and Sanitization
Command Injection
Local
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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