Neural Network Fuzzing macOS Userland (For Fun and Pain)

I have a Mac. Actually, two Macs. A MacBook Pro M3 (my main machine), and an older Mac Pro 6,1 (yeah, the trashcan one). I’m keeping that old beast alive thanks to OpenCore. Over the past two years, I’ve been getting deeper into macOS security—reading Patrick Wardle’s books, dissecting Ventura, Monterey, and running various malware samples. It’s fun, but I wanted to actually build something.

The Idea: A Neural Network-Powered Fuzzer

I’ve been thinking about fuzzers. Everyone’s writing kernel fuzzers these days. I get it. Kernel bugs pay better and can be cooler. But honestly, macOS kernel security is a black box. XNU is massive. Between the BSD parts and the Mach parts and Apple’s hardened runtime… it’s just a lot. I wasn’t ready to start kernel fuzzing. But I could attack userland.

But what kind of fuzzer? Well, neural networks are cool, and I love working with AI. So why not combine the two? Build a neural network that learns syscall patterns, then throw those sequences at macOS userland, blindly, like a drunk hacker at 2AM. Would I find any 0days? Probably not. But watching my Mac freak out and maybe crash sounded like a decent weekend.

Step 1: Harvesting Syscalls with DTrace

First problem: get syscalls. Apple’s syscalls.master file is public but parsing it is miserable. Apple prefers developers use high-level APIs. But with dtrace, I could record real syscalls:

sudo dtrace -n 'syscall:::entry { printf("%s(%x, %x, %x, %x, %x, %x)", probefunc, arg0, arg1, arg2, arg3, arg4, arg5); }' > syscall_log.txt

I scripted random file operations, network requests, and permission prompts to trigger a variety of syscalls. After hours, I had a fat syscall_log.txt.

Step 2: Preprocessing into Training Data

I wrote a Python script to parse these calls and save them as JSON sequences. Each syscall was mapped as:

{
  "syscall": "unknown_5",
  "args": [0x100000004, 0, 0, 0, 0, 0]
}

Each sequence had 100 syscalls per JSON file. I ended up with 2,000+ JSON files.

Step 3: Building and Training the Neural Network

I went with a Convolutional Neural Network (CNN). Why CNN? In my experience, CNNs handle sequential, positional data better than vanilla feed-forward networks.

class SysCallNet(nn.Module):
    def __init__(self, input_dim=7, output_dim=7, hidden_dim=512):
        super(SysCallNet, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv1d(input_dim, hidden_dim, kernel_size=3, padding=1),
            nn.BatchNorm1d(hidden_dim),
            nn.ReLU(),
            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=3, padding=1),
            nn.BatchNorm1d(hidden_dim),
            nn.ReLU(),
            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=3, padding=1),
            nn.BatchNorm1d(hidden_dim),
            nn.ReLU()
        )
        self.output_layer = nn.Conv1d(hidden_dim, output_dim, kernel_size=3, padding=1)

    def forward(self, x):
        x = self.encoder(x)
        return torch.sigmoid(self.output_layer(x))

I added nn.BatchNorm1d(hidden_dim) after each convolution layer to stabilize training. Without it, I noticed the gradients were exploding midway through training. Batch normalization helped keep the activations under control, which led to smoother convergence. Afterwards, training loss dropped nicely over 3 epochs. No, this wasn’t a GPT-4 model, but the network learned syscall patterns well enough to generate valid, semi-realistic sequences.

Step 4: Generating Payloads

The model could now generate benign syscall sequences. But I also injected known malicious sequences (reverse shells, file droppers) at random points:

[
  {"syscall": "unknown_5", "args": [0x100000002, 577, 0o777, 0, 0, 0]},
  {"syscall": "unknown_4", "args": [4, 0x100000003, 512, 0, 0, 0]},
  {"syscall": "unknown_6", "args": [4, 0, 0, 0, 0, 0]}
]

This way, the model didn’t just generate benign traffic—it learned to occasionally behave maliciously.

Step 5: Fuzzing with C and Forked Processes

Here’s where things got chaotic. I wrote a C fuzzer that reads JSON files, parses the syscalls and arguments, then calls syscall() directly. Each JSON file is handled in a forked child process. Why? Because if (when) it crashes, the parent stays alive and continues fuzzing:

pid_t pid = fork();
if (pid == 0) {
    // Child process: execute syscalls from JSON
    Fuzz_Syscalls_From_File(filepath);
    exit(0);
} else if (pid > 0) {
    // Parent process: wait for child
    int status;
    waitpid(pid, &status, 0);
    if (WIFSIGNALED(status)) {
        int sig = WTERMSIG(status);
        fprintf(crash_log, "[CRASH] File %s crashed (signal %d)\n", filepath, sig);
    }
}

This simple fork/wait architecture prevented my entire fuzzer from dying every time a syscall sequence killed a process.

Disaster: VMs Are Pain

And then, Parallels broke.

I snapshot my VM thinking I’d be safe. I wasn’t. The snapshot bricked my VM. I couldn’t click, couldn’t type. Reverting made it worse. Parallels’ snapshot system is trash. Then I tried UTM. It let me clone VMs, but guess what? Each VM is 500GB. I quickly burned through all my disk space.

I now firmly believe snapshots in Parallels/UTM on macOS are cursed. I spent more time recovering than fuzzing.

Lessons Learned

Future Plans

Moving forward, I want to:

Final Thoughts

Is this practical? Honestly? Probably not. But it’s fun. Neural network-based fuzzing, blindly throwing syscalls at macOS userland, watching crashes happen, and logging everything feels like hacking in the movies. Will I find a real 0day? Doubtful. But I’m learning, and sometimes that’s enough.

I plan to realease the code after I clean it up a bit and add some more features.