CVE-2025-14847 Elastic Security · Elastic

Detect MongoDB Improper Handling of Length Parameter Inconsistency (CVE-2025-14847) in Elastic Security

Detects exploitation attempts targeting CVE-2025-14847, an improper handling of length parameter inconsistency vulnerability (CWE-130) in MongoDB and MongoDB Server. This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog, indicating active exploitation in the wild. Attackers may craft malformed requests with inconsistent length parameters to cause unexpected server behavior, potentially leading to denial of service, data corruption, or unauthorized access.

MITRE ATT&CK

Tactic
Initial Access Impact

Elastic Detection Query

Elastic Security (Elastic)
eql
sequence by host.name with maxspan=5m
  [process where process.name in ("mongod", "mongos") and
   event.type == "start"]
  [any where
   (data_stream.dataset : "mongodb.*" or log.logger : "mongod") and
   (
     message : "*length*inconsisten*" or
     message : "*BSONObj*invalid*" or
     message : "*assertion*failed*" or
     message : "*malformed*request*" or
     message : "*param*mismatch*"
   )
  ]
high severity medium confidence

EQL sequence detection correlating MongoDB process activity with log events indicating length parameter inconsistency exploitation (CVE-2025-14847).

Data Sources

Elastic EndpointMongoDB IntegrationSystem Logs

Required Tables

logs-mongodb.*logs-system.syslog*metrics-mongodb.*

False Positives & Tuning

  • MongoDB process restarts during patching or upgrades coinciding with transient error log entries
  • Misconfigured MongoDB client drivers sending non-standard BSON messages triggering parser errors
  • Automated health-check scripts that probe MongoDB and generate benign error responses
  • High-cardinality write workloads causing transient BSON length validation warnings

Other platforms for CVE-2025-14847


Testing Methodology

Validate this detection against 3 adversary techniques from Atomic Red Team. Each test below lists the behaviour to exercise and the telemetry you should expect to see. Executable commands and cleanup steps are available with Pro.

  1. Test 1Send Malformed BSON Message with Inconsistent Length to MongoDB

    Expected signal: MongoDB diagnostic log should record a parse error, assertion failure, or connection reset. Network capture should show malformed wire protocol frame followed by TCP RST or server-side close.

  2. Test 2Fuzzing MongoDB Port with Length Boundary Payloads

    Expected signal: Multiple connection events on port 27017 in rapid succession, followed by MongoDB log entries for parser errors or assertion failures for each boundary value tested.

  3. Test 3Verify MongoDB Version and Patch Status

    Expected signal: MongoDB audit log (if enabled) records a connection and command execution from localhost. Authentication log shows anonymous or credential-less connection attempt.


Response Playbook

Triage

  1. Identify all MongoDB instances in the environment and cross-reference against affected version list; prioritize internet-exposed or externally reachable MongoDB nodes.
  2. Review MongoDB diagnostic logs (default: /var/log/mongodb/mongod.log) for assertion failures, BSON parsing errors, or length inconsistency messages occurring around the alert timestamp.
  3. Check MongoDB server version using 'db.version()' or 'mongod --version' on affected hosts; determine if a patched version is available and has been applied.
  4. Correlate source IPs of suspicious connections to MongoDB ports (27017-27019) against threat intelligence feeds and internal asset inventory to distinguish internal from external actors.
  5. Review MongoDB access logs and authentication events for unusual client connections, especially unauthenticated access or connections from unexpected IP ranges.

Containment

  1. Immediately restrict network access to MongoDB ports (27017, 27018, 27019) using firewall rules or security groups, allowing only known application servers and DBA hosts.
  2. If active exploitation is confirmed, isolate the affected MongoDB host from the network and initiate snapshot/backup of the data directory before any remediation to preserve forensic evidence.
  3. Rotate all MongoDB credentials and connection string secrets; revoke and regenerate API keys or Vault leases associated with MongoDB access.

Evidence Collection

  1. Collect MongoDB diagnostic logs (/var/log/mongodb/mongod.log), system logs (/var/log/syslog or /var/log/messages), and network capture (pcap) of traffic on MongoDB ports during the attack window.
  2. Export MongoDB oplog entries from the affected timeframe using 'db.oplog.rs.find({ts: {$gte: Timestamp(<start>), $lte: Timestamp(<end>)}})' to identify unauthorized write or read operations.
  3. Capture process listing, open file handles, and network connections from the MongoDB host at time of detection using 'ps aux', 'lsof -i :27017', and 'ss -tulnp'.

Escalation Criteria

  • !Escalate immediately if evidence of data exfiltration is found, such as large outbound transfers from the MongoDB host to external IPs or unexpected dump files in the filesystem.
  • !Escalate if the MongoDB instance contains PII, PHI, financial records, or other sensitive data and any unauthorized access or modification is confirmed, triggering breach notification obligations.

Investigation Guide

Related Techniques

Forensic Artifacts

  • >MongoDB diagnostic log entries containing assertion failures, BSON parsing errors, or length parameter warnings at /var/log/mongodb/mongod.log
  • >Network PCAP showing malformed MongoDB wire protocol messages with inconsistent length fields targeting port 27017/27018/27019
  • >MongoDB profiler output (db.system.profile) capturing slow or failed queries from attacker IP addresses during the exploitation window
  • >OS-level audit logs (auditd) recording unexpected mongod process crashes or restarts coinciding with attack activity

Tuning Guidance

Reduce false positives by baselining normal MongoDB assertion and error rates per host over a 30-day period and alerting only on statistically significant deviations. Exclude known maintenance windows and scheduled backup job timeframes. Scope the detection to production MongoDB instances only, excluding dev/QA environments. If MongoDB audit logging is enabled, enrich alerts with authenticated username to distinguish legitimate DBA activity from anonymous or unexpected client connections. Consider raising confidence to 'high' once a specific patched version boundary is confirmed in the MongoDB advisory.


Hunting Queries

Retrospective 7-day hunt across MongoDB log sources for repeated assertion and BSON error patterns that may indicate CVE-2025-14847 probing or exploitation attempts preceding the initial alert.

Hunting — KQL
kql
Syslog
| where TimeGenerated >= ago(7d)
| where ProcessName has_any ("mongod", "mongos")
| where SyslogMessage has_any ("assertion", "exception", "BSON", "length", "malformed")
| summarize event_count=count(), first_seen=min(TimeGenerated), last_seen=max(TimeGenerated) by Computer, ProcessName, SyslogMessage
| where event_count > 5
| order by event_count desc
Hunting — SPL
spl
index=* sourcetype IN ("mongod", "mongodb", "syslog") earliest=-7d
| regex _raw="(?i)(assertion|exception|bson|length|malformed)"
| stats count as hits, earliest(_time) as first_seen, latest(_time) as last_seen by host, sourcetype
| where hits > 5
| sort - hits

Atomic Red Team Tests

Test 1 Send Malformed BSON Message with Inconsistent Length to MongoDB
linux

Simulates CVE-2025-14847 by crafting a raw MongoDB wire protocol message with a declared length field that does not match the actual payload length, targeting the vulnerability in the BSON parser.

Command

bash
python3 -c "
import socket, struct
# Craft a MongoDB OP_MSG with inconsistent messageLength header
payload = b'\x00' * 20  # intentionally short body
msg_length = 9999  # declared length far exceeds actual payload
request_id = 1
response_to = 0
op_code = 2013  # OP_MSG
header = struct.pack('<iiii', msg_length, request_id, response_to, op_code)
malformed = header + payload
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(('127.0.0.1', 27017))
s.send(malformed)
try:
    resp = s.recv(4096)
    print('Response received:', resp[:50])
except Exception as e:
    print('Exception (expected):', e)
finally:
    s.close()
"

Cleanup

bash
No persistent changes; connection is closed after test. Verify mongod process is still running with 'systemctl status mongod' or 'ps aux | grep mongod'.

Expected Telemetry

MongoDB diagnostic log should record a parse error, assertion failure, or connection reset. Network capture should show malformed wire protocol frame followed by TCP RST or server-side close.

Expected Detection

Triggers on MongoDB log assertion/BSON error patterns; network-based detections should fire on malformed OP_MSG to port 27017.

Test 2 Fuzzing MongoDB Port with Length Boundary Payloads
linux

Uses a simple fuzzing loop to send multiple BSON messages with varying declared lengths versus actual body sizes to probe MongoDB's length parameter handling across boundary conditions.

Command

bash
python3 -c "
import socket, struct, time
target = ('127.0.0.1', 27017)
for declared_len in [0, 1, 4, 16, 256, 65535, 2147483647]:
    try:
        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        s.settimeout(2)
        s.connect(target)
        body = b'\xde\xad\xbe\xef' * 4
        header = struct.pack('<iiii', declared_len, 2, 0, 2013)
        s.send(header + body)
        resp = s.recv(1024)
        print(f'len={declared_len}: got {len(resp)} bytes back')
    except Exception as e:
        print(f'len={declared_len}: {type(e).__name__}: {e}')
    finally:
        s.close()
    time.sleep(0.1)
"

Cleanup

bash
No persistent changes; all sockets are closed. Run 'systemctl status mongod' to confirm service health post-test.

Expected Telemetry

Multiple connection events on port 27017 in rapid succession, followed by MongoDB log entries for parser errors or assertion failures for each boundary value tested.

Expected Detection

High-frequency connection bursts to MongoDB port combined with repeated error log entries should trigger rate-based and pattern-based detection rules.

Test 3 Verify MongoDB Version and Patch Status
linux

Enumerates the MongoDB version running on the target host to determine whether the instance is running a vulnerable version, simulating reconnaissance an attacker would perform before exploiting CVE-2025-14847.

Command

bash
mongosh --host 127.0.0.1 --port 27017 --eval 'db.version(); db.adminCommand({buildInfo:1}).version; db.adminCommand({buildInfo:1}).gitVersion' --quiet 2>/dev/null || mongo --host 127.0.0.1 --port 27017 --eval 'db.version()' --quiet 2>/dev/null || python3 -c "
import socket, struct, json
s = socket.socket()
s.connect(('127.0.0.1', 27017))
# OP_QUERY on admin.$cmd for isMaster to fingerprint version
query = b'\x00' * 4 + b'{\"isMaster\": 1}'
header = struct.pack('<iiii', len(query)+16, 1, 0, 2004)
s.send(header + b'\x00' * 4 + b'admin.\$cmd\x00' + struct.pack('<ii', 0, 1) + query)
print('Version probe sent; check MongoDB logs for connection')
s.close()
"

Cleanup

bash
No changes made to the MongoDB instance. Connection is closed after version enumeration.

Expected Telemetry

MongoDB audit log (if enabled) records a connection and command execution from localhost. Authentication log shows anonymous or credential-less connection attempt.

Expected Detection

Unauthenticated buildInfo or isMaster command from a non-application source IP may trigger anomalous access detection; confirms vulnerability recon phase.

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