GHSA-CC4V-RVGP-2PF3

Vulnerability from github – Published: 2026-09-23 21:23 – Updated: 2026-09-23 21:23
VLAI
Summary
Jawn: Uncontrolled nesting depth in JSON parser
Details

The Jawn parser before 1.6.1 is vulnerable to a denial of service attack via untrusted input.

Impact

A remote attacker who can submit JSON to any jawn-backed parse method can exhaust JVM heap and trigger java.lang.OutOfMemoryError. This is treated by Scala as a fatal error and not typically handled by scala.util.Try or cats.effect.IO.

Patches

Version 1.6.1 introduces a configurable nesting-depth limit (Parser#maxDepth, default 4096). Inputs deeper than the limit fail with a recoverable ParseException instead of exhausting heap.

Users who require deeper nesting may override maxDepth on a Parser subclass.

Workarounds

  • Enforce an input size limit small enough that the resulting context stack cannot exhaust heap, (e.g. http4s EntityLimiter).
  • Pre-scan untrusted input and reject documents whose maximum delimiter nesting exceeds a threshold before handing them to jawn.
Severity
7.5 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Show details on source website
To clipboard 

{
  "affected": [
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 1.6.0"
      },
      "package": {
        "ecosystem": "Maven",
        "name": "org.typelevel:jawn-parser_2.12"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.7.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 1.6.0"
      },
      "package": {
        "ecosystem": "Maven",
        "name": "org.typelevel:jawn-parser_2.13"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.7.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 1.6.0"
      },
      "package": {
        "ecosystem": "Maven",
        "name": "org.typelevel:jawn-parser_3"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.7.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-59990"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-770"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-09-23T21:23:54Z",
    "nvd_published_at": null,
    "severity": "HIGH"
  },
  "details": "The Jawn parser before 1.6.1 is vulnerable to a denial of service attack via untrusted input.\n\n### Impact\n\nA remote attacker who can submit JSON to any jawn-backed parse method can exhaust JVM heap and trigger `java.lang.OutOfMemoryError`.  This is treated by Scala as a fatal error and not typically handled by `scala.util.Try` or `cats.effect.IO`.\n\n### Patches\n\nVersion `1.6.1` introduces a configurable nesting-depth limit (`Parser#maxDepth`, default `4096`).  Inputs deeper than the limit fail with a recoverable `ParseException` instead of exhausting heap.\n\nUsers who require deeper nesting may override `maxDepth` on a `Parser` subclass.\n\n### Workarounds\n\n- Enforce an input size limit small enough that the resulting context stack cannot exhaust heap, (e.g. http4s `EntityLimiter`).\n- Pre-scan untrusted input and reject documents whose maximum delimiter nesting exceeds a threshold before handing them to jawn.",
  "id": "GHSA-cc4v-rvgp-2pf3",
  "modified": "2026-09-23T21:23:54Z",
  "published": "2026-09-23T21:23:54Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/typelevel/jawn/security/advisories/GHSA-cc4v-rvgp-2pf3"
    },
    {
      "type": "WEB",
      "url": "https://github.com/typelevel/jawn/commit/191cb3a44e77f1afab439ee636bf66bdf3c54a04"
    },
    {
      "type": "WEB",
      "url": "https://github.com/typelevel/jawn/commit/6219666641f9408498f85868f835e17bd8a72fed"
    },
    {
      "type": "WEB",
      "url": "https://github.com/typelevel/jawn/commit/93ac93e9c992c11b4c03d5455d8551f9fb24da1b"
    },
    {
      "type": "WEB",
      "url": "https://github.com/typelevel/jawn/commit/f6ace7e0db715de1a8c4618bed9378333a5c2214"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/typelevel/jawn"
    },
    {
      "type": "WEB",
      "url": "https://github.com/typelevel/jawn/releases/tag/v1.7.0"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Jawn: Uncontrolled nesting depth in JSON parser"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.





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.



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.