Cyber Attacks Against AI Infrastructure Are in The Rise With Key Vulnerabilities Uncovered

Cyber-criminals have gradually shifted their focus toward the high-value infrastructure that trains, tunes and serves modern artificial-intelligence models.

Over the past six months, incident-response teams have documented a new malware family, tentatively dubbed “ShadowInit,” that targets GPU clusters, model-serving gateways and orchestration pipelines inside large language model (LLM) deployments.

Unlike earlier crypto-mining campaigns, ShadowInit seeks to exfiltrate proprietary weights and silently manipulate inference outputs, undermining trust in downstream applications such as fraud-detection systems and autonomous-driving stacks.

Initial telemetry shows that ShadowInit gains entry by abusing widely shared model-training notebooks that rely on unpinned package versions. Once a victim pulls the notebook, a poisoned dependency fetches an ELF dropper compiled for NVIDIA’s CUDA runtime.

Trend Micro analysts noted the threat after a burst of anomalous outbound traffic from an East-Coast research lab running A100 clusters, eventually tracing the binaries to an actor group with overlaps to the BianLian ransomware crew.

The same group is suspected of offering “model leak” data sets on darknet forums for as little as $5,000 per 100 MB bundle.

ShadowInit’s impact is both immediate and residual. Immediate losses include unexpected GPU time consumption—averaging 6,400 GPU-hours per breach—and forced downtime for integrity checks.

The residual threat is harder to quantify: stolen weights enable adversaries to craft highly realistic phishing content or to fine-tune rival models at a fraction of the cost.

In one manufacturing incident, a tampered vision model misclassified safety-critical defects, triggering a 47-minute assembly-line halt that cost an estimated $1.3 million in revenue.

Closer binary analysis reveals a modular structure. A lightweight loader performs environment checks, then dynamically reconstructs the main payload from base64-encoded chunks stored in otherwise benign Jupyter-metadata fields.

This captures a memory snapshot where the reconstructed payload sits inside pinned GPU buffers, effectively hiding from traditional user-space scanners. Importantly, the loader disables NVIDIA’s Compute Sanitizer hooks, thwarting attempts to intercept rogue kernels.

Because campaign operators understand that AI infrastructure is monitored by DevOps teams rather than security specialists, they embed deceptive log entries.

For example, ShadowInit forges kube-audit messages to mimic routine autoscaling events, pushing genuine alerts below the fold of most dashboards.

Infection Mechanism via Container Side-Loading

ShadowInit’s preferred infection vector is a malicious OCI-layer that masquerades as a legitimate CUDA base image. When developers execute a seemingly harmless docker pull cuda:12.5-base, the registry returns a manipulated manifest that swaps layer digests on the fly.

The following Go snippet, extracted from the attacker’s registry proxy, demonstrates how the digest substitution occurs in transit:-

func rewriteManifest(w http.ResponseWriter, r *http.Request, legit, evil string) {
    body, _ := io.ReadAll(r.Body)
    manifest := bytes.ReplaceAll(body, []byte(legit), []byte(evil))
    w.Header().Set("Content-Type", "application/vnd.oci.image.manifest.v1+json")
    w.Write(manifest) // sends doctored manifest downstream
}

Upon container start, the injected layer mounts /dev/nvidia0 with cap_sys_rawio privileges and deploys a tiny eBPF program that intercepts GPU DMA writes.

This grants persistent read-only snapshots of inference traffic without altering kernel code, satisfying the attacker’s need for stealth.

The side-loaded layer then schedules a cron-style job inside the container namespace that periodically pipes stolen tensors through an AES-GCM tunnel to a cloudflare-workers endpoint, evading outbound firewall rules by mimicking telemetry to public model hubs.

Detection remains challenging because file-integrity monitoring tools often ignore volatile container layers, and GPU-level hooks are rarely audited.

Nonetheless, defenders can mitigate risk by enforcing image-signature verification, pinning dependency versions inside notebooks, and forwarding GPU firmware logs to SIEM pipelines for anomaly detection.

Trend Micro researchers recommend deploying runtime attestation agents capable of hashing live model weights and comparing them against known-good baselines every 15 minutes, a strategy that would have surfaced ShadowInit’s tampering within the first hour of execution.

Equip your SOC with full access to the latest threat data from ANY.RUN TI Lookup that can Improve incident response -> Get 14-day Free Trial