CVE-2026-9749
Analyzed Analyzed - Analysis Complete

MongoDB Aggregation Pipeline Key-Range Buffer Overflow

Vulnerability report for CVE-2026-9749, including description, CVSS score, EPSS score, affected products, exploitability, helpful resources, and attack-flow context.

Publication date: 2026-06-09

Last updated on: 2026-06-18

Assigner: MongoDB, Inc.

Description

This issue can occur when running an aggregation pipeline that uses the internal $exchange stage configured with key-range partitioning and order-preserving delivery. If a single key range produces enough documents to fill its exchange buffer (that is, many results are routed to the same consumer), the server reaches the code path where a full per-consumer buffer is detected but the internal "high watermark" for that key range is not updated as intended.

CVSS Scores

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Meta Information

Published
2026-06-09
Last Modified
2026-06-18
Generated
2026-06-30
AI Q&A
2026-06-10
EPSS Evaluated
2026-06-28
NVD
EUVD

Affected Vendors & Products

Showing 4 associated CPEs
Vendor Product Version / Range
mongodb mongodb From 8.0.0 (inc) to 8.0.24 (exc)
mongodb mongodb From 8.2.0 (inc) to 8.2.10 (exc)
mongodb mongodb From 8.3.0 (inc) to 8.3.3 (exc)
mongodb mongodb From 7.0.0 (inc) to 7.0.35 (exc)

Helpful Resources

Exploitability

CWE
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KEV
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CWE ID Description
CWE-617 The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.

Attack-Flow Graph

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Executive Summary

This vulnerability occurs when running an aggregation pipeline in MongoDB that uses the internal $exchange stage with key-range partitioning and order-preserving delivery. If a single key range produces enough documents to fill its exchange buffer (meaning many results are routed to the same consumer), the server reaches a code path where a full per-consumer buffer is detected but the internal "high watermark" for that key range is not updated as intended.

Impact Analysis

The vulnerability can lead to issues in the aggregation pipeline processing, potentially causing disruptions or failures when the exchange buffer for a consumer becomes full and the internal tracking (high watermark) is not updated correctly. This can impact the reliability and performance of data processing operations that rely on this mechanism.

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