Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:H/SI:H/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-35459 is a critical-severity SSRF (CWE-918) vulnerability in Pyload-Ng Project Pyload-Ng. Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 20th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-35459 is a server-side request forgery (SSRF) vulnerability, classified under CWE-918, affecting pyLoad, a free and open-source download manager written in Python. The issue impacts versions 0.5.0b3.dev96 and earlier. It arises as a bypass of the SSRF fix for CVE-2026-33992, which added IP validation in the BaseDownloader.download() method to check the hostname of the initial download URL. However, pycurl is configured with FOLLOWLOCATION=1 and MAXREDIRS=10, allowing automatic following of HTTP redirects without validating redirect targets against the SSRF filter.
An authenticated user with ADD permission can exploit the vulnerability by submitting a malicious URL that redirects to an internal address, bypassing the SSRF protections. This enables the attacker to forge requests to internal network resources from the pyLoad server. The vulnerability has a CVSS v3.1 base score of 9.1 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N), reflecting high impacts on confidentiality and integrity.
Mitigation details are available in the pyLoad GitHub security advisory at https://github.com/pyload/pyload/security/advisories/GHSA-7gvf-3w72-p2pg and the fixing commit at https://github.com/pyload/pyload/commit/33c55da084320430edfd941b60e3da0eb1be9443.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-19481
Vulnerability Data
pyLoad is a free and open-source download manager written in Python. In 0.5.0b3.dev96 and earlier, pyLoad has a server-side request forgery (SSRF) vulnerability. The fix for CVE-2026-33992 added IP validation to BaseDownloader.download() that checks the hostname of the initial download…
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URL. However, pycurl is configured with FOLLOWLOCATION=1 and MAXREDIRS=10, causing it to automatically follow HTTP redirects. Redirect targets are never validated against the SSRF filter. An authenticated user with ADD permission can bypass the SSRF fix by submitting a URL that redirects to an internal address.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.