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CVE-2026-33314 (GCVE-0-2026-33314)

Vulnerability from cvelistv5 – Published: 2026-03-24 18:52 – Updated: 2026-03-26 19:52
VLAI
Title
pyload-ng: Improper Authentication and Origin Validation Error
Summary
pyLoad is a free and open-source download manager written in Python. Prior to version 0.5.0b3.dev97, a Host Header Spoofing vulnerability in the @local_check decorator allows unauthenticated external attackers to bypass local-only restrictions. This grants access to the Click'N'Load API endpoints, enabling attackers to remotely queue arbitrary downloads, leading to Server-Side Request Forgery (SSRF) and Denial of Service (DoS). This issue has been patched in version 0.5.0b3.dev97.
SSVC
Exploitation: poc Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-26 19:33 UTC
CWE
References
Impacted products
Vendor Product Version CPE status
pyload pyload Affected: < 0.5.0b3.dev97
guessed Create a notification for this product.
Show details on NVD website

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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

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Detection rules are retrieved from Rulezet.

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Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.


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