MongoDB is a widely used, open-source NoSQL database that stores data in flexible BSON (binary JSON) documents instead of traditional rows and tables. Its flexible schema, horizontal scaling, and rich ecosystem make it popular across industries such as: fintech, healthcare, etc.

The Vulnerability: MongoBleed

CVE-2025-14847, known as MongoBleed, is a heap-memory disclosure vulnerability in MongoDB Server. It arises in the server’s zlib compression handling logic, specifically in how it parses compressed network messages. By sending specially crafted messages with inconsistent length fields, an attacker can cause MongoDB to return uninitialized heap memory, potentially exposing sensitive in-memory data, without any authentication.

• Root cause: message_compressor_zlib.cpp returns the allocated buffer length instead of the actual decompressed length, leading to memory over-reads, similar in effect to Heartbleed.
• Attack vector: Fully remote; occurs prior to authentication, requiring only network access to MongoDB’s default port (27017).
• Impact: High confidentiality loss (CVSS v3.1 score 7.5, v4.0 score 8.7) without integrity or availability effects.

Affected Versions

8.2.x < 8.2.3, 8.0.x < 8.0.17, 7.0.x < 7.0.28, 6.0.x < 6.0.27, 5.0.x < 5.0.32, 4.4.x < 4.4.30, All 4.2.x, 4.0.x, and 3.6.x versions.

Please see MongoDB's security advisory for patch instructions.

Exploitation and Ease of Use

A public proof-of-concept (PoC) exploit was released on Dec 26, 2025 and is available on GitHub. Security researchers reported exploitation in the wild shortly after disclosure. While some early blog posts mischaracterized it as RCE, MongoDB and NVD confirm it's a read-only memory disclosure, though leaked secrets can enable further compromise.

Scale of the Problem: Internet Exposure

Censys reports approximately 87,000 internet-accessible MongoDB instances potentially vulnerable to MongoBleed. Shodan-based estimates suggest 100,000+ exposed instances, serving as a general scale estimate. While these counts may include patched or non-zlib-enabled systems, they underscore the vast attack surface facing organizations worldwide.

Detection Signals in Traffic & Logs

PoC exploit activity generates extreme connection bursts, often exceeding 50,000–100,000 connections per minute, unlike normal usage. Attack sessions do not include client metadata (event ID 51800), though legitimate drivers always send this. Logs include connection (22943) and disconnection (22944) events, with missing metadata entries.

Detection strategy: aggregate logs by source IP, and flag:

• ≥ 100 total connections
• < 10% metadata presence
• connection rates ≥ 500/min

IPs matching these criteria are high-risk, indicating possible MongoBleed activity.

Be advised that this information is based on the current public PoC. A more sophisticated threat-actor could alter the PoC or develop a new one to generate traffic that could look benign.

Why It Matters: Sensitive Data at Risk

MongoDB often handles sensitive information, BSON documents containing PII, authentication credentials, tokens, keys, and operational metadata. Memory leaks may expose authentication tokens and secrets, database session data, and PII. Even a read-only leak can enable credential compromise, leading to data theft or full system takeover.

Mitigation & Response Checklist

1. Patch immediately to: 8.2.3, 8.0.17, 7.0.28, 6.0.27, 5.0.32, 4.4.30, or newer.
2. If patching is delayed: Disable zlib compression using networkMessageCompressors without zlib, Restrict network access to trusted IPs only.
3. Enable verbose JSON logging to track connection metadata and parsing errors.
4. Scan logs for bursty connections with missing metadata from suspicious IPs.
5. If exploitation is suspected, rotate secrets such as tokens, keys, and credentials that may have been leaked.

How Varonis Helps Protect Against MongoBleed

Varonis delivers comprehensive MongoDB protection through:

• Varonis for Network: Monitor spikes in inbound/outbound organizational network traffic and receive valuable insights on traffic that could imply on real-time attacks being carried.
• Data Security Posture Management: Assessing MongoDB instances’ data and its sensitivity, detect vulnerable configurations and prioritize remediation based on sensitive data holdings.
• Database Activity Monitoring (DAM): Behavioral analytics detect high-velocity connection anomalies and missing metadata patterns; correlate alerts with data sensitivity.
• Managed Data Detection & Response (MDDR): Enrich alerts with incident context and data sensitivity, automate containment steps, provided by our incident response and forensics professionals.