For years, the bottleneck in software was “how fast can we write code?” Today, Generative AI shifts that bottleneck to “how fast can we secure it”.

For years, the bottleneck in software was “how fast can we write code?” Today, Generative AI shifts that bottleneck to “how fast can we secure it?”

As organizations move from experimentation to production-grade AI, they are discovering that traditional DevOps tooling wasn’t built for a non-deterministic world. For example, static software composition analysis (SCA) scanners that assume deterministic dependency graphs or CI policy gates that validate known build artifacts. When a model generates code rather than a human, the software supply chain changes overnight.

Our guide, Securing non-deterministic systems: A practical guide for AI artifacts and LLMOps, explores three emerging security frontiers that every organization adopting AI must address:

1. AI-generated code introduces supply-chain hallucinations

LLMs generate dependencies probabilistically, not deterministically. This creates the emerging slopsquatting attack vector, where attackers register hallucinated package names suggested by AI tools and weaponize them with malicious payloads.Without validation and artifact governance, a single copied command can silently compromise an enterprise environment.

2. AI models behave like executable software, not passive data

Modern model formats can execute arbitrary code during deserialization, most notably through Python pickle-based loading.This logic-weight entanglement means downloading an unverified model from public registries such as Hugging Face or Ollama can result in full system compromise.Secure AI development requires scanning, signing, and favoring restricted formats like safetensors, alongside enforcing trusted provenance for every model artifact.

3. AI productivity and orchestration layers expand the attack surface

Frameworks that connect models to enterprise data and automate workflows introduce a new class of high-impact vulnerabilities.Recent RCE exploits in orchestration tools demonstrate that LLMOps infrastructure itself is now part of the software supply chain, and must be sandboxed, authenticated, and governed like any production system.

Ready to harden your AI supply chain?

Our full guide provides a strategic roadmap for navigating the shift from DevOps to LLMOps, deconstructing threats in frameworks like Langflow, and building a “sandbox-by-default” development lifecycle.

[Download the full guide: Securing non-deterministic systems]