CVE-2018-1295

What is this vulnerability?

CVE-2018-1295 is categorized as a critical Deserialization of Untrusted Data flaw with a CVSS base score of 9.8. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

In Apache Ignite 2.3 or earlier, the serialization mechanism does not have a list of classes allowed for serialization/deserialization, which makes it possible to run arbitrary code when 3-rd party vulnerable classes are present in Ignite classpath. The vulnerability can be exploited if the one sends a specially prepared form of a serialized object to one of the deserialization endpoints of some Ignite components - discovery SPI, Ignite persistence, Memcached endpoint, socket steamer.

This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.

Risk Assessment

Impact on Systems

✅ Data Exfiltration: Attackers can extract sensitive data from backend databases, configuration files, or internal services.

✅ Authentication Bypass: Exploiting this flaw may allow unauthorized access to protected resources and administrative interfaces.

✅ Lateral Movement: Once initial access is gained, attackers can pivot to internal systems and escalate privileges.

How to Fix and Mitigate CVE-2018-1295

1. Apply Vendor Patches: Upgrade affected components to their latest, non-vulnerable versions immediately.
2. Implement Input Validation: Ensure all user-supplied data is validated, sanitized, and type-checked before processing.
3. Deploy Runtime Protection: Use Precogs continuous monitoring to detect exploitation attempts in real time.
4. Audit Dependencies: Review and update all third-party libraries and transitive dependencies.

Defending with Precogs AI

Precogs AI Analysis Engine identifies this vulnerability class through semantic code analysis powered by Code Property Graph (CPG) technology, performing inter-procedural taint tracking to detect injection flaws, broken authentication, and insecure data flows across your entire codebase.

Use Precogs to continuously scan your codebase, binaries, APIs, and infrastructure for this vulnerability class and related attack patterns. Our AI-powered detection engine combines static analysis with threat intelligence to identify exploitable weaknesses before attackers do.

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Vulnerability Code Signature

Attack Data Flow

Vulnerable Code Pattern

// ❌ VULNERABLE: Unsafe deserialization
public Object deserialize(byte[] data) throws Exception {
    ByteArrayInputStream bais = new ByteArrayInputStream(data);
    ObjectInputStream ois = new ObjectInputStream(bais);
    // Taint sink: instantiates arbitrary classes
    return ois.readObject();
}

Secure Code Pattern

// ✅ SECURE: Type-restricted deserialization
public Object deserialize(byte[] data) throws Exception {
    ByteArrayInputStream bais = new ByteArrayInputStream(data);
    // Use ValidatingObjectInputStream (Apache Commons IO)
    ValidatingObjectInputStream ois = new ValidatingObjectInputStream(bais);
    ois.accept(SafeClass.class);
    // Sanitized instantiation
    return ois.readObject();
}

How Precogs Detects This

Precogs AI Analysis Engine natively intercepts unsafe deserialization sinks to prevent remote code execution via object instantiation.