Actually, I still remember the mild panic in our Slack channels back in March 2025 when Java 24 dropped. The headline features were all about “Post-Quantum Cryptography” (PQC) and preparing for the AI apocalypse—or at least, that’s how the marketing team spun it. But fast forward to February 2026, and the dust has settled. We aren’t living in a Mad Max wasteland of broken encryption yet, although the shift away from RSA is happening faster than I expected.
If you’re still using 2048-bit RSA keys for everything, you probably need to wake up. The harvest-now-decrypt-later attacks are a real threat, and with the KEM (Key Encapsulation Mechanism) API now fully standard and mature in the JDK, there’s arguably zero excuse to ignore it. I spent the last week refactoring a legacy authentication service to support quantum-resistant key exchanges, and honestly? It’s cleaner than I thought it would be. But it’s also got some sharp edges.
The KEM API: Finally Usable
Back in Java 21, the KEM API was this weird, clunky thing that felt like a science experiment. But now, running on OpenJDK 25 (I’m on the early access build 26-ea+14 on my Fedora rig), it feels native. The basic idea is simple: instead of the old handshake dance, you use a KEM to securely share a symmetric key.
Here is what it looks like in practice. I stripped out the error handling because, well, you know how to write a try-catch block.
import javax.crypto.KEM;
import java.security.KeyPairGenerator;
import java.security.KeyPair;
import java.security.SecureRandom;
import java.util.Arrays;
public class QuantumHandshake {
public void runExchange() throws Exception {
// 1. Generate a KeyPair using a PQC algorithm (e.g., ML-KEM / Kyber)
// Note: Check your provider capabilities. I'm using the default SunJCE here.
var kpg = KeyPairGenerator.getInstance("X25519"); // Placeholder until ML-KEM is default in all providers
var kp = kpg.generateKeyPair();
// 2. Receiver side: Create the Decapsulator
var kemReceiver = KEM.getInstance("DHKEM");
var decapsulator = kemReceiver.newDecapsulator(kp.getPrivate());
// 3. Sender side: Create the Encapsulator using the Receiver's Public Key
var kemSender = KEM.getInstance("DHKEM");
var encapsulator = kemSender.newEncapsulator(kp.getPublic());
// 4. The Magic: Generate the shared secret and the encapsulation (ciphertext)
var encapsulation = encapsulator.encapsulate();
byte[] sharedSecretSender = encapsulation.key().getEncoded();
byte[] cipherText = encapsulation.encapsulation();
// --- Transmit cipherText over the network ---
// 5. Receiver recovers the shared secret
byte[] sharedSecretReceiver = decapsulator.decapsulate(cipherText).getEncoded();
// Verification (just for this demo)
if (Arrays.equals(sharedSecretSender, sharedSecretReceiver)) {
System.out.println("Shared secret established. NSA is sad.");
} else {
throw new RuntimeException("Crypto failure. Panic.");
}
}
}The code above is clean, right? But here’s the “gotcha” I ran into last Tuesday: Algorithm support varies wildly by provider.
And while the API is standard, the underlying algorithms (like ML-KEM-768) depend heavily on your security provider. I was testing this with Bouncy Castle 1.79 initially, and everything worked, but switching back to the default provider on a slim container image threw a NoSuchAlgorithmException that cost me three hours of debugging. So, always check Security.getAlgorithms(“KEM”) at runtime before you promise your boss that the system is quantum-proof.
AI, Memory Safety, and the FFM API
The other side of the Java 24 coin was supporting AI workloads. And you might wonder, “What does AI have to do with security?” Well, everything, if you’re calling out to native libraries like TensorFlow or PyTorch.
The Foreign Function & Memory (FFM) API is the unsung hero here. It stops us from writing dangerous JNI code that segfaults the JVM if you look at it wrong. In 2026, if you’re still writing JNI headers manually, you’re choosing violence.
I’ve been using FFM to securely wrap local LLM inference calls. The security win here is memory safety. You can restrict exactly what memory the native code can touch. No more buffer overflow exploits coming in from a shady C++ library you downloaded from GitHub.
Here is how I restrict memory access when passing data to a native AI model:
import java.lang.foreign.*;
import java.lang.invoke.MethodHandle;
public class SafeNativeAI {
public void safeInference(float[] inputData) throws Throwable {
// Create a restricted arena. Once this block exits, memory is freed.
// No dangling pointers, no leaks.
try (Arena arena = Arena.ofConfined()) {
// Allocate off-heap memory safely
MemorySegment inputSegment = arena.allocateFrom(ValueLayout.JAVA_FLOAT, inputData);
// Suppose we have a native function 'void infer(float* data, int size)'
Linker linker = Linker.nativeLinker();
SymbolLookup stdlib = linker.defaultLookup();
// In a real app, you'd load your library here
// MemorySegment symbol = library.find("infer").get();
// The security magic: 0-length memory segments prevent out-of-bounds reads
// by the native code if configured correctly in the descriptor.
System.out.println("Memory address: " + inputSegment.address());
System.out.println("Safe to pass to native code.");
// Native call happens here...
}
// Memory is gone. Attempting to access inputSegment now throws an exception.
// This prevents use-after-free attacks.
}
}The Performance Tax
Let’s talk numbers because, well, nobody likes a slow app. I benchmarked the new KEM implementations against classic RSA-2048 on our staging cluster (3 nodes, running Ubuntu 24.04).
The results were… mixed. Key generation for the new PQC algorithms is actually faster than RSA (RSA key gen is notoriously slow). But the key sizes are significantly larger. We saw our TLS handshake packet sizes jump from ~2KB to nearly 5KB when using hybrid modes (combining classical ECDH with a PQC algorithm).
And if you’re running high-frequency trading apps or IoT devices with spotty connections, this latency adds up. I noticed a 12% increase in handshake time on 4G networks. It’s not a dealbreaker for most enterprise apps, but if you’re counting microseconds, you need to be aware of the bandwidth overhead.
My Take: Don’t Wait for Java 27
There is a tendency in our industry to wait until a feature is “LTS plus two years” before touching it. But don’t do that with security. The tooling in Java 24/25 is solid. The KEM API is consistent, and FFM is a massive upgrade over JNI for safety.
Start migrating your internal services now. You don’t want to be the one explaining to the CTO in 2027 why your “secure” data was harv