MongoBleed Forensics

What exploitation leaves behind and what it doesn't

MongoBleed Forensics

CVE-2025-14847, better known as MongoBleed, is a memory disclosure vulnerability in MongoDB’s zlib compression handling that lets unauthenticated attackers pull uninitialized heap memory straight out of the server. It’s conceptually simple, trivial to exploit, and annoying to investigate. A proof-of-concept dropped on Christmas Day 2025, courtesy of Joe Desimone.

The Vulnerability

MongoBleed exploits MongoDB’s network transport compression layer through maliciously crafted compressed payloads that cause the server to miscalculate decompressed data length. The bug lives in how MongoDB handles zlib-compressed network messages.

Here’s the simplified view of what’s happening:

# Conceptual representation of the vulnerable code path
def decompress_message(compressed_data, claimed_size):
    buffer = allocate(claimed_size)  # Allocate based on attacker's claim
    actual_size = zlib.decompress(compressed_data, buffer)
    # Bug: returns buffer.length() instead of actual_size
    return buffer  # Contains uninitialized memory beyond actual_size

The attack works like this:

  1. Attacker sends a compressed message with an inflated uncompressedSize field
  2. MongoDB allocates a buffer based on that claim
  3. zlib decompresses the actual data, which only fills the start of the buffer
  4. MongoDB treats the entire buffer as valid data
  5. BSON parsing reads past the legitimate data into uninitialized heap memory
  6. Server sends the whole thing back to the attacker

The heap is whatever was there before. Previous database operations, credentials, session tokens, internal states, anything that happened to occupy that memory region. You don’t get to choose what you extract. You get what the heap gives you.

Affected Versions and Patch Status

This hits a wide range of MongoDB versions:

Vulnerable:

End-of-life versions (3.6, 4.0, 4.2) aren’t getting patches. If you’re running those, the exposure is permanent. Upgrade or accept the risk.

Proof-of-Concept Details

The original PoC is available at joe-desimone/mongobleed on GitHub. It includes a Docker Compose setup for testing against vulnerable instances, which is convenient for understanding the attack mechanics without wrecking production systems.

The exploit establishes rapid connections (thousands per minute) and probes for memory leaks at various offsets. Each connection attempts to extract heap fragments by sending malformed compressed messages and parsing whatever comes back.

Example output from a successful exploitation run:

[*] mongobleed - CVE-2025-14847 MongoDB Memory Leak
[*] Author: Joe Desimone - x.com/dez_
[*] Target: 203.0.113.42:27017
[*] Scanning offsets 20-50000

[+] offset=   117 len=   39: ssions^\u0001�r��*YDr���
[+] offset= 16582 len= 1552: MemAvailable: 8554792 kB\nBuffers:
[+] offset= 18731 len= 3908: Recv SyncookiesFailed EmbryonicRsts
[+] offset= 22104 len=  284: "username":"admin","password":"P@ssw0rd123"

[*] Total leaked: 8748 bytes
[*] Unique fragments: 42
[*] Saved to: leaked.bin

That’s 8KB of heap data extracted in seconds. Sometimes you get useful credentials. Sometimes you get kernel statistics. Sometimes you get garbage. The heap doesn’t care about your operational needs.

Exploitation Mechanics

The exploitation process is straightforward but relies on understanding how MongoDB handles compressed network messages.

Network Transport Layer

MongoDB supports multiple compression algorithms for network traffic: snappy, zlib, and zstd. The vulnerability specifically affects zlib compression. When a client connects and negotiates zlib compression, subsequent messages can trigger the memory disclosure.

Message Structure

MongoDB wire protocol messages have this structure:

[messageLength][requestID][responseTo][opCode][messageBody]

When compression is enabled, the message body becomes:

[originalOpcode][uncompressedSize][compressorID][compressedMessageBody]

The uncompressedSize field is where the attack happens. This is a 32-bit integer that tells MongoDB how much space to allocate for decompression. MongoDB trusts this value.

The Attack Sequence

  1. Connection establishment: Attacker connects to MongoDB without authentication
  2. Compression negotiation: Declares zlib support in the handshake
  3. Malicious payload construction: Creates a compressed message where:
    • Actual compressed data is small (e.g., 100 bytes)
    • uncompressedSize field claims a much larger size (e.g., 50,000 bytes)
  4. Memory allocation: MongoDB allocates 50,000 bytes based on the claim
  5. Decompression: zlib decompresses the 100 bytes of actual data into the start of the buffer
  6. BSON parsing: MongoDB parses the entire 50,000-byte buffer as BSON data
  7. Response transmission: Server sends parsed data back, including uninitialized memory

The BSON parser doesn’t validate that the data makes sense. It just reads the buffer and tries to construct valid BSON objects from whatever bytes it finds. When it encounters uninitialized memory, it interprets those random bytes as BSON structures and returns them.

What Gets Leaked

The leaked data depends entirely on what was previously allocated in that heap region. Common findings include:

The key point: this is not deterministic. You can’t reliably target specific data. You spray and pray, hoping something useful lands in the extracted fragments.

Considerations and Limitations

Exploitation Limitations

No authentication required: This is pre-auth, which makes it accessible from anywhere the MongoDB port is exposed. If your instance is internet-facing, anyone can attempt exploitation.

Network access required: You need direct access to the MongoDB service port (default 27017). This isn’t exploitable through a web application unless that application directly exposes MongoDB connection functionality.

Compression must be enabled: If zlib compression isn’t supported by the server configuration, the attack fails. Servers configured with only snappy or no compression aren’t vulnerable.

Non-deterministic output: You can’t reliably extract specific data on demand. The heap is dynamic. What you get depends on timing, server load, and what operations happened recently.

High-volume detection signature: The public PoC generates thousands of connections per minute. This is extremely noisy and easily detected in any environment with connection monitoring.

Forensic Limitations

Here’s where it gets uncomfortable.

Memory content uncertainty: Even when you confirm exploitation occurred, you cannot definitively determine what data was exposed. The heap is transient. Memory disclosure vulnerabilities leak unpredictable data. Without complete visibility into heap state at the time of exploitation, you’re guessing.

Log retention gaps: MongoDB logs rotate. If your retention window is 7-14 days (common in many environments) and the exploitation happened before you detected it, the evidence is gone. Detection scripts can’t analyze logs that don’t exist.

Edition-dependent visibility: MongoDB Community Edition, the version most organizations run, has no audit logging. The default verbosity level is -1, which logs only warnings and errors. Even with increased verbosity, you’re looking at connection logs, not operation logs. You can see that connections happened. You can’t see what those connections did.

Credential reuse problem: Attackers can extract credentials via MongoBleed, then use those credentials through normal authenticated channels. The exploitation event is logged (maybe). The subsequent credential abuse looks like legitimate traffic.

This creates a detection/response paradox: you can identify when exploitation attempts occurred, but you cannot prove what data was accessed or what the blast radius actually is.

Detection, Hunting and Blast assessments

Behavioral Signatures

The public PoC has a distinctive signature: thousands of connections per minute with no client metadata.

Every legitimate MongoDB driver sends a metadata message (event ID 51800) immediately after connecting. The exploit does not. This creates a reliable behavioral indicator when the public PoC is used.

Detection logic for Community Edition (with verbosity >= 0):

def detect_mongobleed_exploitation(logs):
    """Analyze MongoDB logs for exploitation indicators."""
    by_ip = defaultdict(lambda: {
        'connections': 0,
        'metadata_events': 0,
        'disconnections': 0,
        'time_window': []
    })
    for event in logs:
        ip = event['remote_ip']
        
        if event['id'] == 22943:  # Connection accepted
            by_ip[ip]['connections'] += 1
            by_ip[ip]['time_window'].append(event['timestamp'])
        elif event['id'] == 51800:  # Client metadata
            by_ip[ip]['metadata_events'] += 1
        elif event['id'] == 22944:  # Connection closed
            by_ip[ip]['disconnections'] += 1
    suspicious = []
    for ip, metrics in by_ip.items():
        if metrics['connections'] < 100:
            continue
        metadata_rate = metrics['metadata_events'] / metrics['connections']
        if len(metrics['time_window']) > 1:
            duration = (max(metrics['time_window']) - 
                       min(metrics['time_window'])).total_seconds() / 60
            velocity = metrics['connections'] / max(duration, 0.1)
        else:
            velocity = 0
        if metadata_rate < 0.1 and velocity > 1000:
            suspicious.append({
                'ip': ip,
                'connections': metrics['connections'],
                'metadata_rate': metadata_rate,
                'velocity': velocity,
                'risk': 'HIGH'
            })
    return suspicious

Detection output when the public PoC is used:

$ ./mongobleed-detector.sh /var/log/mongodb/

╔══════════════════════════════════════════════════════╗
║     MongoBleed (CVE-2025-14847) Detection Results   ║
╚══════════════════════════════════════════════════════╝

[INFO] Processing MongoDB logs...
[INFO] Analyzed 7 days of logs (2025-12-21 to 2025-12-27)
[INFO] Total connections analyzed: 142,847

Risk     SourceIP         ConnCount  MetaCount  MetaRate%  BurstRate/m
-------- ---------------- ---------- ---------- ---------- --------------
HIGH     203.0.113.42     8,234      0          0.0%       112,453

[INFO] Analysis complete

That’s a clear indicator when the public exploit is used: zero metadata messages, 112,453 connections per minute. Legitimate applications don’t behave like this.

Log Analysis for Community Edition

If you’re running Community Edition, your forensic options are limited but not zero. Three critical log components exist:

“NETWORK” events - Connection and disconnection:

Example output:

"{""t"":{""$date"":""2025-12-27T20:53:11.142+00:00""},""s"":""I"",  ""c"":""NETWORK"",  ""id"":22943,   ""ctx"":""listener"",""msg"":""Connection accepted"",""attr"":{""remote"":""203.0.113.42:60696"",""uuid"":{""uuid"":{""$uuid"":""75531bc6-21c8-967b-89ed-5d28e22e72f5""}},""connectionId"":21,""connectionCount"":13}}
{""t"":{""$date"":""2025-12-27T20:53:11.144+00:00""},""s"":""I"",  ""c"":""NETWORK"",  ""id"":22944,   ""ctx"":""conn21"",""msg"":""Connection ended"",""attr"":{""remote"":""203.0.113.42:60696"",""uuid"":{""uuid"":{""$uuid"":""75531bc6-21c8-967b-89ed-5d28e22e72f5""}},""connectionId"":21,""connectionCount"":10}}"
"{""t"":{""$date"":""2025-12-27T20:53:21.321+00:00""},""s"":""I"",  ""c"":""NETWORK"",  ""id"":51800,   ""ctx"":""conn24"",""msg"":""client metadata"",""attr"":{""remote"":""203.0.113.42:54188"",""client"":""conn24"",""negotiatedCompressors"":[],""doc"":{""application"":{""name"":""mongodump""},""driver"":{""name"":""mongo-go-driver"",""version"":""1.16.0""},""os"":{""type"":""linux"",""architecture"":""amd64""},""platform"":""go1.21.12""}}}

The absence of “received client metadata” indicates the connection never sent metadata—a MongoBleed indicator.

“ACCESS” events - Authentication:

Example:

{""t"":{""$date"":""2025-12-27T20:04:49.119+00:00""},""s"":""I"",  ""c"":""ACCESS"",   ""id"":5486106, ""ctx"":""conn13"",""msg"":""Successfully authenticated"",""attr"":{""client"":""203.0.113.42:40898"",""isSpeculative"":true,""isClusterMember"":false,""mechanism"":""SCRAM-SHA-256"",""user"":""root"",""db"":""database1"",""result"":0,""metrics"":{""conversation_duration"":{""micros"":2213,""summary"":{""0"":{""step"":1,""step_total"":2,""duration_micros"":165},""1"":{""step"":2,""step_total"":2,""duration_micros"":30}}}},""extraInfo"":{}}}

“COMMAND” events - Limited based on verbosity but a good hunt if you have an increased verbosity level and want to confirm any potential DB interactions

Detection Tools

Two primary tools exist for MongoBleed detection:

Velociraptor Artifact Linux.Detection.CVE202514847.MongoBleed Developed by Eric Capuano at Recon InfoSec, this artifact parses MongoDB logs using Velociraptor’s incident response platform.

$ velociraptor artifacts collect \
    Linux.Detection.CVE202514847.MongoBleed \
    --args LogPath=/var/log/mongodb/ \
    --args TimeWindow=3600 \
    --args MetadataThreshold=0.1 \
    --args VelocityThreshold=1000

Standalone Script Neo23x0/mongobleed-detector Developed by Florian Roth (creator of THOR APT Scanner), this shell script analyzes MongoDB JSON logs offline.

git clone https://github.com/Neo23x0/mongobleed-detector.git
cd mongobleed-detector
chmod +x mongobleed-detector.sh
./mongobleed-detector.sh /var/log/mongodb/

Both tools analyze the same behavioral signatures: connection velocity and metadata absence. Both suffer from the same limitations: log dependency, evasion susceptibility, and inability to quantify breach impact.

Evasion Considerations

The metadata-based detection is effective against the public PoC but not against adaptive attackers. A motivated threat actor can modify the exploit to send fake client metadata after connecting, making the traffic appear more legitimate.

Modified exploitation that evades metadata-based detection:

def exploit_with_metadata_evasion(target_host, target_port):
    """MongoBleed exploit with fake metadata to evade detection."""
    sock = socket.create_connection((target_host, target_port))
    metadata = build_fake_metadata({
        'driver': {'name': 'PyMongo', 'version': '4.6.1'},
        'os': {'type': 'Linux', 'name': 'Ubuntu', 'version': '22.04'},
        'platform': 'CPython 3.11.7'
    })
    sock.send(metadata)
    malicious_payload = craft_mongobleed_payload(offset=1000)
    sock.send(malicious_payload)
    response = sock.recv(8192)
    leaked_data = parse_bson_for_memory(response)
    sock.close()
    return leaked_data

This reduces exploitation speed but improves stealth. Detection based solely on metadata absence fails against this variant. The velocity signature remains, but high-throughput legitimate applications may trigger false positives, creating alert fatigue.

The uncomfortable truth: detection identifies exploitation attempts with the public PoC, not all exploitation, and definitely not data exfiltration success.

Remediation and Workarounds

Eliminate the exposure of the MongoDB service

Patching

Apply the fixed versions:

If you’re on end-of-life versions (3.6, 4.0, 4.2), you need to upgrade to a supported version. There’s no patch coming.

Workaround: Disable zlib Compression

If immediate patching isn’t possible, disable zlib compression:

# mongod.conf - Disable zlib compression
net:
  compression:
    compressors: snappy  # Remove 'zlib' from list

Or via command line:

mongod --networkMessageCompressors snappy

This prevents exploitation but may impact performance in bandwidth-constrained environments. The trade-off between security and performance isn’t always straightforward, especially in high-throughput production systems.

Forensic Readiness Improvements

For organizations running MongoDB in production, the following baseline configurations improve forensic capability:

For Community Edition:

For Enterprise Edition:

Post-Exploitation Response Checklist

When MongoBleed exploitation is confirmed:

### Confirmed Exploitation Response

## Immediate Actions
- [ ] Isolate affected MongoDB instances from the network
- [ ] Preserve all available logs before rotation
- [ ] Snapshot VM/container for forensic analysis
- [ ] Document timeline of detected exploitation attempts
- [ ] Verify MongoDB edition (Community vs Enterprise)
- [ ] Check audit logging status if Enterprise

## Evidence Collection
- [ ] Collect all MongoDB JSON logs (entire retention period)
- [ ] Check current verbosity level: `db.getLogComponents()`
- [ ] If verbosity was `-1`, acknowledge limited forensic evidence
- [ ] Grep logs for NETWORK, ACCESS, and COMMAND events
- [ ] Collect MongoDB audit logs (if enabled and Enterprise)
- [ ] Collect system logs (auth.log, syslog, kern.log)
- [ ] Collect network flow data (NetFlow, firewall logs)
- [ ] Collect process execution logs (auditd, sysmon)

## Scope Assessment (Edition-Dependent)
- [ ] Identify all source IPs with suspicious connection patterns
- [ ] Correlate exploitation timeframe with other security events
- [ ] Review what data was in memory during exposure window
- [ ] Assess credential/token exposure risk
- [ ] **If Community Edition**: Assume worst-case data access
- [ ] **If Enterprise with audit**: Analyze actual operations performed

## Credential Rotation (Assume Compromise)
- [ ] Rotate all MongoDB authentication credentials
- [ ] Rotate application database credentials
- [ ] Rotate API keys/tokens that may have been in heap memory
- [ ] Review and rotate service account credentials
- [ ] Search for use of old credentials in authentication logs

## Lateral Movement Analysis
- [ ] Check for unauthorized access using potentially leaked credentials
- [ ] Review authentication logs across environment for suspicious activity
- [ ] Scan for indicators of compromise across infrastructure
- [ ] Assess blast radius if credentials were reused elsewhere
- [ ] **If audit logs available**: Identify specific collections accessed
- [ ] **If no audit logs**: Assume all data potentially compromised

The “assume compromise” approach isn’t paranoia. It’s practical. Memory disclosure vulnerabilities leak unpredictable data. Without complete visibility into heap state at the time of exploitation, conservative assumptions are appropriate.

Closing Assessment

MongoBleed is trivial to exploit, widely applicable, and difficult to investigate forensically. The exploitation mechanics are straightforward. The forensic artifacts are clear when the public proof-of-concept is used. They become ambiguous when attackers adapt their tooling.

Three forensic realities define this vulnerability:

  1. Detection identifies exploitation attempts, not data exfiltration success
    You can see connections. You might see authentication. You probably can’t see what was in the heap when it was leaked.

  2. Log retention determines investigation feasibility more than detection sophistication
    Detection scripts are useless if the logs rotated before you ran them.

  3. Memory disclosure scope cannot be determined definitively after the fact
    The heap is dynamic. What was leaked depends on what was there. You can’t prove what wasn’t there.

The gap between “we detected exploitation attempts” and “we know what data was compromised” is where incident response breaks down. Detection is useless without evidence. Evidence is useless without retention. Retention is useless without analysis capability. Analysis capability requires the right edition with the right configuration.

If you’re running Community Edition with default verbosity at -1 and 7-day log retention, your forensic visibility is effectively zero. You might see high connection volumes in firewall logs. You won’t see what happened next.

Patch if you can. Disable zlib if you can’t. Improve your logging before you need it. And maybe consider whether Community Edition is giving you the forensic capability you think you have.

References