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CVE-2025-48934 (GCVE-0-2025-48934)

Vulnerability from cvelistv5 – Published: 2025-06-04 19:21 – Updated: 2025-06-04 19:32
VLAI
Title
Deno.env.toObject() ignores the variables listed in --deny-env and returns all environment variables
Summary
Deno is a JavaScript, TypeScript, and WebAssembly runtime. Prior to versions 2.1.13 and 2.2.13, the `Deno.env.toObject` method ignores any variables listed in the `--deny-env` option of the `deno run` command. When looking at the documentation of the `--deny-env` option this might lead to a false impression that variables listed in the option are impossible to read. Software relying on the combination of both flags to allow access to most environment variables except a few sensitive ones will be vulnerable to malicious code trying to steal secrets using the `Deno.env.toObject()` method. Versions 2.1.13 and 2.2.13 contains a patch.
Severity
5.5 (Medium)
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N/E:P
SSVC
Exploitation: poc Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-06-04 19:32 UTC
CWE
  • CWE-201 - Insertion of Sensitive Information Into Sent Data
Assigner
References
Impacted products
Vendor Product Version CPE status
denoland deno Affected: < 2.1.13
Affected: >= 2.2.0, < 2.2.13
guessed Create a notification for this product.
Show details on NVD website
To clipboard 

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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.

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  • 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.


Detection rules are retrieved from Rulezet.


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.