Critical Flaws in NVIDIA Merlin Could Let Attackers Execute Code and Disrupt AI Systems
The race toward smarter AI just hit a security speed bump.
NVIDIA has released emergency patches for Merlin, its open-source machine learning framework used to power large-scale recommender systems. Two newly discovered high-severity deserialization vulnerabilities could allow attackers to execute malicious code, steal sensitive data, or trigger denial-of-service (DoS) attacks on Linux systems.
The flaws — CVE-2025-33214 and CVE-2025-33213 — were detailed in NVIDIA’s December 9, 2025 security bulletin and affect the NVTabular and Transformers4Rec components of the Merlin ecosystem.
Both vulnerabilities carry CVSS base scores of 8.8 (High), meaning exploitation could have a serious impact across AI-driven production environments.
💥 The Core Issue: Deserialization Gone Wrong
At the root of both flaws is CWE-502: Deserialization of Untrusted Data — a classic software weakness that can open the door to remote exploitation.
Here’s what’s at stake:
CVE-2025-33214 — Impacts the Workflow component in NVTabular
CVE-2025-33213 — Impacts the Trainer component in Transformers4Rec
Attackers could manipulate serialized objects to inject malicious payloads, gain code execution, and manipulate AI model data or training results.
The attack path is network-based (AV:N) — meaning exploitation can occur remotely with minimal complexity and no authentication required.
While limited user interaction is needed, successful exploitation could result in:
Arbitrary code execution 🧠
Privilege escalation 🔐
Confidential data exposure 📂
AI model corruption or service disruption ⚠️
For organizations running Merlin in production, these vulnerabilities effectively turn a trusted AI framework into a potential attack surface for adversaries targeting machine learning pipelines.
🧩 Patch Now: NVIDIA’s Official Fix
NVIDIA strongly recommends all Merlin users update immediately to the latest secure versions:
NVTabular: Update to commit 5dd11f4 or later
Transformers4Rec: Update to commit 876f19e or later
Older versions remain vulnerable and should be considered compromised until updated.
Updates can be obtained directly from the official NVIDIA Merlin GitHub repositories.
These vulnerabilities were responsibly disclosed by security researcher blazingwind, acknowledged by NVIDIA’s Product Security Incident Response Team (PSIRT) for responsible reporting.
🧠 Why This Matters
Merlin is widely used across industries for recommendation engines, personalization, and predictive analytics. A single breach could impact:
E-commerce personalization systems
Media streaming recommendation engines
Financial and retail analytics platforms
Insecure AI frameworks don’t just risk downtime — they can compromise the integrity of machine learning outputs, leading to corrupted predictions and long-term business disruption.
With AI rapidly becoming the backbone of digital operations, vulnerabilities like these highlight an uncomfortable truth: AI infrastructure security is lagging behind its adoption.
🔐 The Elliptic Systems Perspective
The NVIDIA Merlin vulnerabilities aren’t just isolated bugs — they’re a preview of what’s coming as AI pipelines become prime targets.
AI frameworks like Merlin, TensorFlow, and PyTorch are increasingly part of the supply chain of decision-making, and any compromise can ripple through data ecosystems.
At Elliptic Systems, we help enterprises:
Conduct AI infrastructure penetration testing to uncover vulnerabilities in frameworks and models
Deploy zero-trust controls around machine learning environments
Implement secure development lifecycle (SDLC) practices for AI codebases
Strengthen compliance with ISO 42001 and AI governance frameworks
Because securing AI isn’t optional — it’s essential to protect the trust behind intelligent automation.
👉 Schedule an AI Security Assessment
⚠️ Final Takeaway
NVIDIA’s patched vulnerabilities in Merlin are a warning for every organization leveraging AI in production:
machine learning code is software — and software can be exploited.
As AI transforms business operations, cybercriminals are moving upstream — targeting the tools that build intelligence itself.
The future of AI security depends on one principle: patch fast, audit often, and never trust unchecked code.
Elliptic Systems — Defending AI, Protecting Innovation.
