Background

Shortly before Christmas 2025, security researcher Joe Desimone disclosed CVE-2025-14847, a high-severity memory disclosure vulnerability in MongoDB Server rated CVSS 8.7. The vulnerability, dubbed "MongoBleed", affects MongoDB Servers when zlib network message compression is enabled.

The attack vector is unauthenticated and remote, requiring only the processing of a specially crafted compressed message to achieve exploitation. Given MongoDB's widespread use in web applications, content management systems, and backend services, the potential impact spans numerous industries including finance, healthcare, government, and technology sectors.

Technical Details

As discussed by Ox Security here, the vulnerability stems from improper handling of length parameter inconsistencies in zlib-compressed protocol header parsing within MongoDB Server.

Many applications expose MongoDB ports to internal networks or, in misconfigured environments, directly to the internet. In both scenarios, simply sending malicious compressed messages is sufficient to trigger the vulnerability, with no user interaction or authentication beyond network access required.

Furthermore, if default command line parameters are used, there won’t be any visibility of the attack within MongoDB audit logs. To detect this effectively would entail having either an Intrusion Detection System (IDS) monitoring the traffic going to and from the affected MongoDB server, or MongoDB command logging to be enabled.  To enable MongoDB command logging, which will show BSON parsing errors and slow queries, add the following to your MongoDB init command line:

--setParameter “logComponentVerbosity={network: 0, command: 1, accessControl: 0, query: 0}” --quiet

This will show you BSON parsing and assertion errors which can be triaged and responded to:

{

    "t": {

           "$date": "2025-12-27T18:01:10.195+00:00"

    },

    "s": "I",

    "c": "COMMAND",

    "id": 51803,

    "ctx": "conn8159",

    "msg": "Slow query",

    "attr": {

           "type": "none",

           "isFromUserConnection": true,

           "ns": "",

           "collectionType": "none",

           "numYields": 0,

           "ok": 0,

           "errMsg": "incorrect BSON length in element with field name 'a' in object with unknown _id",

           "errName": "InvalidBSON",

           "errCode": 22,

           "reslen": 166,

           "locks": {},

           "cpuNanos": 113281,

           "remote": "172.19.0.1:51379",

           "numInterruptChecks": 0,

           "queues": {

                  "ingress": {},

                  "execution": {}

           },

           "workingMillis": 0,

           "durationMillis": 0

    }

}

In addition to the “Slow query” logs, which will be very numerous from the attacking source IP, there will also be error logs with the msg value “Assertion while parsing command”:

{

    "t": {

           "$date": "2025-12-27T18:01:10.198+00:00"

    },

    "s": "D1",

    "c": "COMMAND",

    "id": 21963,

    "ctx": "conn8160",

    "msg": "Assertion while parsing command",

    "attr": {

           "error": "InvalidBSON: incorrect BSON length in element with field name 'a' in object with unknown _id"

    }

}

The barrage of “Slow query” MongoDB log msg values currently appears to be the most valuable MongoDB log, as it also contains the source IP address and port of the attacker. A default MongoBleed attack generated over 8k of each of the log lines.

Note, incident responders may require firewall or other network logs to marry the source connection information to the actual attacker dealing with network address translation (NAT).

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Affected Products

Vulnerable Versions:

The vulnerability affects multiple versions of MongoDB Server dating back to version 3.6:

·  MongoDB < 8.2.3

·  MongoDB < 8.0.17

·  MongoDB < 7.0.28

·  MongoDB < 6.0.27

·  MongoDB < 5.0.32

·  MongoDB < 4.4.30

·  MongoDB 4.2 – All versions, no fix available

·  MongoDB 4.0 – All versions, no fix available

·  MongoDB 3.6 – All versions, no fix available

Patched Versions:

• MongoDB 8.2.3+
• MongoDB 8.0.17+
• MongoDB 7.0.28+
• MongoDB 6.0.27+
• MongoDB 5.0.32+
• MongoDB 4.4.30+

Indicators of Compromise

Organizations should create mechanisms to centralize MongoDB command error logging and use that to triage large spikes (>1k) in “Slow query” messages, which could indicate MongoBleed is being used against the affected system. The presence of hundreds or thousands of “InvalidBSON: incorrect BSON length in element with field name” is also highly indicative of intentional BSON tampering which could lead to sensitive information disclosure.

When the MongoBleed attack was run against a vulnerable server for testing, by default it generated over 8k “Slow query” log messages. Spikes of this log message over 1k should be triaged and investigated immediately.

Attack Signatures & Log Search Queries:

• Large spike (>1k) of “Slow query” msg with errMsg containing “incorrect BSON length in element with field name”
• Large spike (>1k) in errCode = 22

Post-Exploitation Indicators:

• CPU and memory contention from a large spike in malformed requests
• Large amounts of data being requested from an IP which has not authenticated successfully

Organizations identifying these indicators should immediately initiate incident response procedures and conduct comprehensive mitigation and forensic analysis.

Recommendations

Organizations using MongoDB should take immediate action to patch this critical vulnerability.

Immediate Actions:

• Upgrade MongoDB to patched versions referenced in the Patched Versions section
• Enable command error logging to be able to detect suspicious and malicious MongoDB queries

Risk Assessment:

• Identify all MongoDB deployments and ensure they have proper logging and network IDS visibility enabled
• Review network segmentation to determine if MongoDB instances can be segmented further

Immediate Workarounds (if patching is not immediately feasible):

• Disable ZLIB compression support from within MongoDB until a patch can be applied

Detection and Monitoring:

• Enable comprehensive logging for all document processing workflows, including full request payloads where feasible.
• Implement real-time monitoring for large spikes (>1k “Slow query” logs) in errors caused on the MongoDB systems
• Correlate “Slow query” logs with upstream network traffic to ensure rapid mitigation for ongoing attacks

Response Planning:

• Prepare incident response procedures for potential exploitation, including system isolation steps and forensic log collection requirements.
• Consider implementing defense-in-depth measures including Intrusion Detection Systems (IDS) with volumetric detection rules against any databases, though these should not replace patching.

Conclusion

CVE-2025-14847 represents a high severity vulnerability in a very widely deployed database, MongoDB. With unauthenticated remote exploitation this vulnerability demands immediate attention from every organization using MongoDB.

Ensuring that your MongoDB system has appropriate network segmentation is paramount to reducing the attack surface for this exploit. Validating that only required systems have network access to your MongoDB instance will help ensure less exploitation.

In addition, if possible, command-level debug logging should be enabled with:

--setParameter “logComponentVerbosity={network: 0, command: 1, accessControl: 0, query: 0}” --quiet

In doing so, security teams gain clear visibility into performance issues as well as exploitation attempts, through the distinctive InvalidBSON errors generated during attacks. Combined with network flow analysis for connection anomaly detection, organizations can build robust detection capabilities for this vulnerability class.

The logging configuration described here balances detection fidelity with operational overhead, providing the specific visibility needed to detect this threat. With proper use of security data pipelines, many of these log messages can even be aggregated to provide high fidelity visibility, without consuming large quantities of disk space, or bogging down your security teams.

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Appendix A

Example logs from MongoDB with MongoBleed being run against it.