GHSA-H24G-PJPF-8F2M

Vulnerability from github – Published: 2026-10-02 12:31 – Updated: 2026-10-02 12:31
VLAI
Details

Memory allocation with excessive size value vulnerability in Apache Directory LDAP API.

A malicious peer (or a MITM) can send a small BER-encoded response causing a large memory allocation before any data is received. This can lead to an OutOfMemoryError and denial of service.

The client JVM OOMs (OutOfMemoryError bypasses the DecoderException handlers) or pins the large allocation per connection while the attacker stalls.

A handful of connections exhausts any heap. The same bytes from an unauthenticated pre-bind client hit any embedding server that did not set MAX_PDU_SIZE_ATTR.

This issue affects Apache Directory LDAP API: from 1.2.0 before 1.2.9.

Users are recommended to upgrade to version 1.2.9, which fixes the issue.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-102731"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-789"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-10-02T10:17:04Z",
    "severity": null
  },
  "details": "Memory allocation with excessive size value vulnerability in Apache Directory LDAP API.\n\n\n\nA malicious peer (or a MITM) can send a small BER-encoded response causing a large memory allocation before any data is received. This can lead to an OutOfMemoryError and denial of service.\n\n\n\nThe client JVM OOMs (OutOfMemoryError bypasses the DecoderException handlers) or pins the large allocation per connection while the attacker stalls.\n\n\n\nA handful of connections exhausts any heap. The same bytes from an unauthenticated pre-bind client hit any embedding server that did not set MAX_PDU_SIZE_ATTR.\n\n\n\nThis issue affects Apache Directory LDAP API: from 1.2.0 before 1.2.9.\n\n\n\nUsers are recommended to upgrade to version 1.2.9, which fixes the issue.",
  "id": "GHSA-h24g-pjpf-8f2m",
  "modified": "2026-10-02T12:31:09Z",
  "published": "2026-10-02T12:31:09Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-102731"
    },
    {
      "type": "WEB",
      "url": "https://lists.apache.org/thread.html/b8kg8881pc0v8lp59w0fcfrs69wjbqvd"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}



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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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