incident responseforensicsAI security

Forensic Playbook: Investigating Compromised GPU‑Accelerated Workloads (RISC‑V + NVLink)

UUnknown

2026-02-26

11 min read

Practical IR playbook for GPU breaches: preserve NVLink and GPU memory on RISC‑V hosts, collect logs, and capture volatile state before it disappears.

Hook: Why GPU‑centric Breaches Require a New Playbook

Your threat model just changed. Since 2025 we’ve seen rapid adoption of GPU‑accelerated AI stacks running on non‑x86 hosts—most notably RISC‑V platforms integrating NVIDIA’s NVLink Fusion fabrics (SiFive announced this integration in early 2026). That innovation boosts throughput for AI training and inference, but it also creates a novel, highly volatile forensic domain: GPUs and NVLink fabrics hold critical ephemeral state attackers target for model theft, runtime tampering, and covert persistence. This playbook gives you an operational, technical incident response (IR) workflow to investigate compromised GPU‑accelerated workloads on RISC‑V servers connected via NVLink: what to capture, how to capture it, and how to preserve GPU and NVLink evidence without destroying volatile artifacts.

Inverted‑pyramid summary: Immediate priorities (first 1–2 hours)

Isolate the host to stop lateral movement but avoid rebooting or unloading GPU drivers.
Preserve GPU state immediately—GPU memory and NVLink fabric state are the most volatile evidence.
Capture host volatile data (system RAM, process lists, open sockets) after GPU capture if practical.
Collect persistent logs (kernel, driver, container orchestration logs, DCGM) and hardware topology (PCIe/NVLink).
Create cryptographic hashes and document chain‑of‑custody.

Why GPUs and NVLink are different for forensics

GPUs have two properties that complicate standard IR: they maintain large, vendor‑managed device memory separate from host RAM, and inter‑GPU NVLink fabrics enable peer‑to‑peer transfers that can move or hide data off the host quickly. On RISC‑V servers this is compounded by the emerging ecosystem maturity: forensic tooling that runs on x86 Linux may need recompilation or vendor cooperation on RISC‑V. In 2026 expect more organizations to run AI inference and training on heterogeneous stacks—meaning your IR playbook must be GPU‑aware, NVLink‑aware, and architecture‑aware.

Immediate action checklist (First responder, order of volatility)

Follow an order of volatility tailored for GPU incidents. The priority is to preserve anything that will evaporate when drivers unload or GPUs reset.

Isolate network logically — block egress (iptables, cloud security groups) and mirror traffic for capture; avoid full host shutdown.
Document state — take photos of console, logged-in users, system time, attached displays and LEDs.
Collect GPU state (see detailed steps below) — nvlink topology, per‑GPU process list, driver info, and immediate GPU memory capture.
Collect host volatile state — system RAM image (LiME/AVML/winpmem as appropriate), process lists, open ports/sockets.
Collect persistent artifacts — disk images, /var/log, container images, orchestration events, firmware versions.
Hash and store all collected files with SHA‑256; log custody and timestamps.

Step‑by‑step: Capturing GPU & NVLink state

The following subsections list commands, tools, and techniques validated in modern Linux environments (2026) and include RISC‑V considerations where compilation or vendor support may be required.

1) Capture quick metadata (non‑destructive)

Run immediately — these are read‑only queries that help prioritize deeper capture.

Identify GPU model, driver and runtime versions:
```
nvidia-smi -q > /forensic/gpu_nvidia_smi_q.txt
```

List GPU processes and GPU memory usage:

nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv -l 1 -f /forensic/gpu_processes.csv

NVLink and fabric health:

nvidia-smi nvlink -q > /forensic/nvlink_info.txt

Vendor diagnostic bundle (if available):
```
sudo /usr/bin/nvidia-bug-report.sh --logfile /forensic/nvidia_bugreport.tar.gz
```
This script aggregates driver logs, kernel messages and NVML queries — useful for triage and later analysis. Keep the original tarball intact and hash it.

2) Snapshot PCI / topology / firmware

Dump PCI config space and device tree:

lspci -vvv -s <bus:dev.func> > /forensic/pci_dev.txt

Dump MMIO/ BAR info (read‑only):
```
sudo setpci -s <bus:dev.func> -r CAP_EXP+10.l > /forensic/pci_capabilities.hex
```
(Vendor-specific reads may require vendor guidance.)
Record firmware/BIOS and microcode versions:
```
dmidecode -t bios > /forensic/bios.txt
```

3) Capture NVLink fabric telemetry and error counters

NVLink-related counters and fabric manager logs indicate whether data moved between GPUs or across hosts. Collect DCGM (Data Center GPU Manager) and NVML artifacts if available.

DCGM snapshot (if DCGM/ dcgmi installed):

dcgmi dmon -e -f /forensic/dcgmi_dmon.csv

NVML direct queries:

python3 -c "import pynvml; pynvml.nvmlInit(); print(pynvml.nvmlDeviceGetName(pynvml.nvmlDeviceGetHandleByIndex(0)))" > /forensic/nvml_name.txt

(Useful in environments where the Python runtime is supported; compile pys for RISC‑V if necessary.)

4) Dump GPU device memory (highest volatility)

This is the most time‑sensitive step. Device memory contains model weights, intermediate tensors, encryption keys cached on the device, or staging code used by attackers. Two pragmatic approaches exist: vendor API‑based dumping while the process remains live, or driver/kernel level capture when live APIs are insufficient.

API‑based extraction (preferred when possible)
- Write or use a CUDA/CUPTI/CUDA‑driver program that attaches to the suspicious process and queries its allocations (cuMemGetInfo/cuPointerGetAttributes) and performs cuMemcpy to host buffers. This requires the process to remain alive and may need elevated privileges. Use cuda‑gdb for controlled attachment where permitted by policy.
- Example (conceptual): attach and copy each allocation to /forensic/gpu_mem_pid<pid>_buf<n>.bin. Always checksum each binary.
Driver/kernel capture (when API not possible)
- Capture /dev/nvidia* device state and kernel logs. Some responders use read operations against device nodes to snapshot MMIO or driver buffer areas (only when authorized and tested), and vendor support is strongly advised.
- Use vendor bugreport output — it may contain driver core dumps and allocations metadata.
Important cautions
- Dumping device memory can alter GPU state. If preservation is required for legal reasons, coordinate with legal/compliance and consider full hardware imaging with vendor assistance.
- On RISC‑V nodes you may need to cross‑compile extraction tools or request NVIDIA/RISC‑V‑SoC vendor forensic primitives; maintain vendor communication logs as evidence of requests and replies.

5) Capture host memory and process state

After GPU memory is captured, proceed to capture host memory. On Linux, LiME is the commonly used kernel memory acquisition technique; on Windows, use AVML/winpmem. On RISC‑V Linux, build LiME for the kernel and platform before deployment or get a precompiled image from your vendor.

LiME example (Linux):

insmod lime.ko "path=/forensic/memdump.lime format=lime"

Collect process lists and open sockets:

ps -eo pid,ppid,cmd,%mem,%cpu --sort=-%mem > /forensic/ps.txt
ss -tunaep > /forensic/sockets.txt

For containerized AI workloads, collect:

docker ps -a > /forensic/docker_ps.txt
crictl ps -a > /forensic/crictl_ps.txt
kubectl get pods --all-namespaces -o wide > /forensic/k8s_pods.txt

Log collection: what to preserve

Collect a wide set of logs across host, GPU, NVLink fabric, orchestration, and network layers. Preserve timestamps and use UTC.

Kernel and system journal: journalctl -k -b > /forensic/journal_kernel.txt and dmesg -T > /forensic/dmesg.txt
NVIDIA driver logs and sysfs/proc entries: /proc/driver/nvidia, /sys/module/nvidia, nvidia-smi -q
NVLink and DCGM logs: DCGM diagnostics, NVLink error counters, ECC errors
Container runtime and orchestration logs: Docker, containerd, CRI‑O, Kubernetes events and kubelet logs
Application and model server logs: TorchServe, TensorFlow Serving, Triton Inference Server logs (including model load/unload events)
Authentication and PAM logs; cloud metadata and hypervisor events if VM/container running in cloud

Memory forensic techniques for GPU‑backed workloads

GPU workloads use several types of memory: device global memory, pinned host memory, unified memory (managed), and on‑GPU caches. Forensic analysis must correlate device allocations with host processes and any DMA transfer records.

Map device allocations to PIDs. Use vendor APIs (NVML, CUPTI) to enumerate which PIDs have GPU allocations. Record timestamps and allocation sizes.
Recover tensors and model artifacts. Many model weights are identifiable by entropy and known file fingerprints (e.g., flatbuffers or Protobuf headers). Use carving tools on GPU memory dumps to search for known model signatures or layers.
Search for secrets and keys. Attackers sometimes stage encryption keys or credentials in GPU memory for speed. Use YARA rules and entropy analysis against GPU dumps and pinned host memory.
Trace NVLink transfers. NVLink debug counters may indicate peer‑to‑peer copies; combine DCGM/NVML counters with host network and process timelines to detect offloading to adjacent nodes.

RISC‑V specific considerations

RISC‑V hosts add friction: prebuilt forensic binaries may not exist, and low‑level firmware or vendor integration (e.g., SiFive NVLink Fusion modules) may behave differently. Prepare in advance:

Precompile forensic tools (LiME, Volatility plugins, Python NVML bindings) for your RISC‑V kernels and libc variants. Keep signed, integrity‑checked copies in your IR toolkit.
Maintain serial console access and netconsole for kernel message capture if remote access is lost. On new SoCs, serial output may be the only reliable early indicator.
Coordinate with hardware vendors early. Forensic dumps of device firmware, microcode and NVLink fabric traces frequently require vendor assistance for interpretation and hardware‑level dumps.

Analysis playbook: mapping attack actions to evidence

The goal is to map observed artifacts to attacker techniques and produce actionable conclusions and IOC sets.

Timeline reconstruction — merge GPU allocation timestamps, NVLink counters, kernel logs and network captures into a single timeline (use ELK/Timesketch/Plaso).
Attribution of GPU activity — link allocations to PIDs, container IDs, and user tokens. Correlate with orchestration events (model loads/unloads).
Data exfiltration detection — search for patterns of NVLink peer transfers followed by network egress, or direct PCIe DMA to rogue devices. Look for atypical model serialization or repeated tensor dumps.
Persistence check — inspect startup units, cron jobs, container images, and GPU driver kernel modules for modification. Attackers may hide loaders in GPU‑resident code that reattaches on process restart.

Case study (anonymized, composite — 2025 incident patterns applied to 2026 stacks)

In late 2025 a multi‑tenant AI cluster exhibited intermittent model theft events. Analysis showed attackers exploited a misconfigured model server to load a custom kernel module that allocated pinned host buffers and then used NVLink peer transfers to move those pages to a GPU on a neighboring host before pulling them off the cluster via a compromised GPU‑attached I/O node. The team’s failure to quickly capture GPU device memory lost crucial tensor artifacts. After improving NVLink telemetry, capturing GPU memory early in containment, and coordinating with the GPU vendor to obtain hardware traces, the team reconstructed the attack and closed the exfil path.

Lessons: prioritize GPU memory capture, instrument NVLink/peer counters, and harden model server load paths.

Preservation, evidence handling and legal considerations

Use write‑protect mechanisms for disk evidence and immutable storage for captures. Always compute SHA‑256 and record chain‑of‑custody.
Document every command and operator — GPU dumps are sensitive operations and may require vendor consent.
If hardware seizure is needed, coordinate seizure steps with vendor and lab partners who can conduct cold imaging of GPUs and NVLink fabric traces under forensically sound conditions.

Operational checklist (post‑incident hardening)

Enable and centralize DCGM/NVML and driver audit logs to SIEM with high cardinality retention for GPU telemetry.
Harden model loading endpoints and enforce mutual TLS for model downloads and orchestration APIs.
Segment NVLink fabrics where possible; implement RBAC for GPU allocation requests and host‑level attestation for workloads that request device memory mapping.
Prepackage RISC‑V‑compatible forensic tools and maintain vendor escalation contacts for GPU/NVLink support.

2026 trends and future predictions — prepare for these

More RISC‑V + NVLink deployments: expect vendor‑specific forensic primitives to become available as SiFive‑style integrations proliferate.
Standardization push: by 2026–2027 we expect industry groups to propose GPU forensic primitives (dump APIs, fabric telemetry schemas) to accelerate incident response.
Attack evolution: model theft, GPU‑hosted loaders, and GPU‑backed covert channels will increase — prioritize GPU telemetry and per‑GPU process isolation.
Cloud providers will offer managed GPU forensic snapshots; ensure SLAs and legal access controls are in place for rapid evidence preservation in multitenant environments.

Tools & resources (operational recommendations)

Vendor tools: nvidia-smi, nvidia-bug-report.sh, DCGM (dcgmi), NVML/PyNVML.
Memory acquisition: LiME for Linux (precompile for RISC‑V), AVML/winpmem for Windows hosts.
Forensic analysis: Volatility3 (with custom plugins for GPU artifact analysis), Timesketch/Plaso for timeline analysis.
Network capture and correlation: tcpdump, Zeek, PCAP archival; instrument eBPF-based collectors for high fidelity host telemetry.

Actionable takeaways — what to do now

Inventory: list all GPU hosts (including RISC‑V) and NVLink fabrics, noting driver/firmware versions.
Preposition tools: compile LiME and NVML/PyNVML for your RISC‑V kernels and store integrity‑checked copies in your IR toolkit.
Instrument telemetry: enable DCGM/NVML counters to central logging. Retain high‑resolution NVLink metrics for at least 30 days if possible.
Playbook rehearsal: run tabletop exercises that include GPU memory capture and vendor escalation steps. Test that GPU dumps are readable and hash‑stable in your environment.

Closing / Call to action

GPU forensics is no longer niche—AI‑accelerated environments with NVLink and heterogeneous hosts (including RISC‑V) are core infrastructure in 2026. If you manage GPU clusters, update your IR playbooks now: precompile tools, enable GPU telemetry, and build vendor relationships for hardware‑level support. Need a tailored GPU forensic readiness assessment or incident response support for NVLink‑connected RISC‑V servers? Contact our IR team at antimalware.pro for a fast readiness audit, or download our GPU Forensics checklist to make your IR runbooks NVLink‑ready.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.