Cracking Passwords at AI Speed: Digital Forensics on Grace Blackwell and Unified Memory

Security Sonar

Against a hardened key-derivation function, the decisive advantage isn’t raw compute — it’s never moving data across a bus.

Digital forensics and incident response is under time pressure it wasn’t built for. Attackers now automate with AI and can move through a network in minutes, while the investigative side is still running on corporate laptops and legacy x86 workstations. This post walks through one concrete workflow — recovering an encrypted iPhone backup password — on NVIDIA’s Grace Blackwell architecture, and shows why for this class of target the deciding factor is memory architecture, not raw compute.

All build instructions and code are in the open-source repo: securitysonar/spark-hashcat.

The Golden Hour, and the silent witness

In a kidnapping or missing-persons case the first 60 minutes are when the digital trail is freshest — active cell pings, recent cloud syncs. When a phone is locked and unreachable, the iTunes/Finder backup on a seized workstation is often the investigator’s best route to location history and chat logs. If that backup is encrypted, every minute spent cracking it is a minute of trail going cold.

Why you attack the backup, not the phone

Hashcat is the standard tool for GPU-accelerated password recovery. It can’t target an iPhone directly — the Secure Enclave enforces hardware-backed rate limiting. Instead, investigators target the encrypted iTunes/Finder backup stored on a computer. That vector is valuable for two reasons:

  • Password reuse — the backup password often mirrors the device passcode.
  • Keychain access — cracking the backup unlocks the full keychain: credentials, tokens, and certificates that are otherwise siloed on the device.

Mode 14700 vs. 14800: a deliberate speed bump

With iOS 10.2, Apple reworked how the backup Key Bag is derived, moving the relevant Hashcat mode from 14700 (iOS 9) to 14800 (iOS 10+). Mode 14800 implements a three-stage key derivation function: an initial PBKDF2-SHA256 round, then one million iterations of SHA256, then a final PBKDF2-SHA256 pass. The stages are sequential — each depends on the previous — so they can’t be parallelized across GPU cores the way a fast hash like MD5 can.

The asymmetry is the point:

  • For the legitimate user, the ~10-million-round derivation runs once at password entry — a negligible delay.
  • For an attacker, every guess pays that full cost. Even top-end GPUs drop to a few hundred hashes per second against mode 14800, versus billions per second for fast modes.

Building Hashcat for Grace Blackwell

The DGX Spark runs an ARM64/SBSA architecture. Off-the-shelf security binaries are compiled for x86 and either won’t run or leave most of the platform’s performance on the table, so tools have to be built from source against the Arm CPU and linked to CUDA so the GPU and CPU operate inside the platform’s unified memory.

# Stage 1: build
FROM nvcr.io/nvidia/ai-workbench/python-cuda130:1.0.1 AS builder
RUN apt-get update && apt-get install -y git build-essential libssl-dev
WORKDIR /build
RUN git clone --depth 1 https://github.com/hashcat/hashcat.git .
RUN make

Base images should come from the NVIDIA Container Registry (nvcr.io) — they’re CUDA-optimized and SBSA-validated, with the right driver assumptions baked in. Building on an unvalidated base shifts that burden onto you. The runtime stage also has to map the SBSA/ARM64 Blackwell libraries into place:

# Stage 2: runtime
FROM nvcr.io/nvidia/ai-workbench/python-cuda130:1.0.1
WORKDIR /hashcat
RUN for f in /usr/local/cuda/targets/sbsa-linux/lib/libnvrtc*; do \
      ln -sf "$f" /usr/lib/aarch64-linux-gnu/$(basename "$f"); \
      ln -sf "$f" /usr/lib/aarch64-linux-gnu/$(basename "$f").13.0; \
      ln -sf "$f" /usr/lib/aarch64-linux-gnu/$(basename "$f").12; \
    done && ldconfig

One container-specific gotcha: hardware-monitoring calls that work on bare metal hit a permission boundary inside a properly scoped container runtime. NVML access is meant to be handled at the orchestration layer (NVIDIA Container Toolkit, --gpus device flags), not inside the application — so the fix is to run with --hwmon-disable. Silencing that NVML noise was the difference between a tool that runs and one that performs.

# Hashcat invocation on the DGX Spark, workload profile 4 ("nightmare")
cmd = [
    executable,
    "-m", str(hash_type),          # 14800 for iOS 10+ backups
    "-a", str(attack_mode),        # 3 = mask attack
    f"/hashes/{hash_file}",
    "--quiet",
    "--outfile", output_path,
    "--potfile-disable",
    "--backend-ignore-opencl",
    "--hwmon-disable",
    "--optimized-kernel-enable",
    "-w", "4",
]

The Docker stack around this is straightforward: Traefik as ingress, FastAPI for job submission and status polling, and a background worker that detects the SBSA GPU environment, builds the Hashcat command, and runs it as a subprocess, with volume mounts for hashes, wordlists, and output.

The workflow

With physical access to the Mac or Windows machine holding the sync, extract the hash and salt from the backup’s Manifest.plist. The BackupKeyBag holding the cryptographic keys lives inside that file. philsmd/itunes_backup2hashcat converts it to a Hashcat-readable line:

./itunes_backup2hashcat.pl Manifest.plist | tee itunes_backup.txt

$itunes_backup$*10*795ac5d7f42cebf502ffc9915060a3720bb3a75cfebb61eee2baef7...

The *10* marks it as an iOS 10+ backup — mode 14800. Move the hash to the DGX Spark and submit it through the API:

curl -X POST http://localhost/crack \
  -H "Host: forensics.spark.local" \
  -H "Content-Type: application/json" \
  -d '{
    "hash_type": 14800,
    "attack_mode": 3,
    "hash_file": "itunes_backup.txt",
    "mask": "?d?d?d?d?d?d"
  }'
# {"job_id":"a15d95e5-...","status":"Completed","duration_seconds":1412.96}

1412.96 seconds — 23.5 minutes — to recover a 6-digit numeric password (234567) by exhausting the full keyspace. That turns real-time location history and chat logs into evidence inside a single investigative shift.

The bottleneck isn’t GPU throughput

Mode 14800’s sequential, memory-latency-sensitive iteration is exactly the workload discrete GPUs handle badly: each of the million SHA256 rounds per candidate has to traverse the PCIe bus between CPU and GPU memory, and that latency compounds. The DGX Spark’s GB10 removes the bus. Its Arm CPU and Blackwell GPU share a single coherent memory space over NVLink-C2C — roughly five times the bandwidth of PCIe 5.0 between compute and memory — so intermediate KDF state is handed between stages in place, with no transfer overhead.

In testing, the GB10 resolved the full 6-digit keyspace against mode 14800 in under 24 minutes — less than half the ~55 minutes a discrete RTX 4090 needed for the same job, on a platform with a fraction of the power envelope.

The nvidia-smi readout tells the story: 95% GPU utilization while drawing only 70W. High utilization with low power draw is the signature of a memory-latency bottleneck — the scheduler reports the cores as busy, but they’re mostly stalled on memory reads between KDF stages rather than doing the floating-point or integer work that would push power up. For KDF-hardened targets, memory architecture is the decisive factor.

(Benchmarks run with Hashcat 7.1.2 and CUDA 13.0.)

Where this is heading

SANS’s Protocol SIFT — a research initiative led by Rob Lee, distinct from the SIFT Workstation — is focused on the “speed of defense”: orchestrating and accelerating artifact processing and triage so human responders can match the velocity of AI-enabled adversaries. It pairs naturally with hardware like this: Protocol SIFT handles the intelligent orchestration of an investigation, and a Blackwell GPU with unified memory supplies the raw acceleration to execute the heavy steps in real time.

Cracking a hardened iOS 10+ backup password in under 24 minutes at 70W is a small result with a large implication — for memory-latency-bound forensic workloads, the platform that wins is the one that stops moving data across a bus.

Peter Campbell, CISSP, CEH — Platform Security Engineer, NVIDIA-Certified Professional. Code and deployment guide: securitysonar/spark-hashcat.