Beyond Predictability: Securing Non-deterministic Generative AI in Today's Cyber Landscape

What is Non-deterministic AI?

Unlike traditional software with deterministic logic flows, these non-deterministic generative AI models introduce probabilistic responses that can hallucinate false information, recommend malicious code, or expose sensitive data through unexpected inference patterns. These AI systems produce different outputs for identical inputs, making their behavior inherently unpredictable and difficult to secure.

How do we secure systems that we cannot fully predict or control? The traditional cybersecurity playbook simply doesn't apply to technologies that operate in probability spaces rather than binary logic. Our industry urgently needs new frameworks, methodologies, and security paradigms designed specifically for the non-deterministic nature of modern AI systems.

The unpredictable security challenge

We face an unprecedented security paradox as AI deployment accelerates. This disparity creates critical vulnerabilities, particularly when we examine the fundamental differences between deterministic and non-deterministic AI systems.

Deterministic vs non-deterministic security implications

Traditional deterministic systems produce predictable outputs, enabling straightforward security controls. However, GenAI's non-deterministic nature introduces probabilistic vulnerabilities we're still learning to address. The same prompt can yield different outputs, making security validation complex and threat modeling challenging.

Technical causes of GenAI hallucinations

GenAI hallucinations stem from training data inconsistencies, attention mechanism failures, and overfitting to incomplete patterns. From a security perspective, these hallucinations manifest as significant cybersecurity risks:

• Package hallucinations: 20% of AI-generated code suggestions reference non-existent libraries The average percentage of hallucinated packages is at least 5.2 percent for commercial models and 21.7 percent for open source models

• Slopsquatting: Attackers exploit these hallucinations by creating malicious packages with AI-suggested names

Prompt injection evolution

Prompt injection attacks are evolving beyond direct manipulation. There have been cases of sophisticated indirect injections where malicious instructions are hidden in external data sources, such as PDFs or web content, that the AI processes. A seemingly innocent customer feedback form could contain hidden prompts that compromise the AI's behavior.

MITRE ATLAS framework application

The MITRE ATLAS framework categorizes these adversarial threats systematically. Techniques like ML Supply Chain Compromises (AML.T0010) directly correlate with package hallucination vulnerabilities, while Prompt Injection addresses the growing injection attack surface.

Critical vulnerabilities of non-deterministic AI

As AI systems become integral to our security infrastructure, we're confronting unprecedented vulnerabilities that demand immediate attention. 

Key vulnerability categories

Model manipulation attacks

• Prompt injection attacks exploiting probabilistic components to bypass security controls.

• Adversarial inputs designed to corrupt model decision-making processes

• Model inversion techniques extracting sensitive training data

Training data poisoning

• Malicious data injection during model training phases

• Backdoor attacks embedding hidden triggers in AI systems

• Data corruption affecting model reliability and accuracy

Supply chain risks

• Third-party model dependencies introducing unknown vulnerabilities

• Pre-trained model contamination from upstream sources

• Insufficient vetting of AI development frameworks and libraries

Over-reliance issues

• Junior developers showing over-reliance on AI-generated code without proper validation

• Automated decision-making systems operating without adequate human oversight

• Critical security functions delegated to unverified AI outputs

Why traditional testing falls short

The OWASP GenAI guidelines specifically address why conventional security testing approaches fail for non-deterministic systems. Traditional penetration testing assumes predictable system responses, but AI systems' probabilistic nature creates blind spots in our security assessments.

We need adaptive testing methodologies that account for:

• Non-deterministic outputs requiring statistical validation

• Model drift affecting long-term security posture

• Context-dependent vulnerabilities emerging from training data

Building effective defenses

We're witnessing a critical shift in AI security approaches as organizations recognize the need for comprehensive defense frameworks. The landscape has evolved rapidly, demanding structured methodologies to address emerging threats.

Leading AI security frameworks (2024-2025)

• NIST AI Risk Management Framework (AI RMF) provides the most comprehensive lifecycle approach, covering governance, risk mapping, measurement, and management across AI development phases. NIST released the Generative AI Profile (NIST-AI-600-1) in July 2024, which specifically addresses GenAI risks. We recommend this as the foundational framework for enterprise implementations.

• Microsoft's AI security framework specifically targets adversarial threats, offering robust protection against model poisoning and prompt injection attacks. Their approach emphasizes threat modeling and security-by-design principles.

• Google's SAIF (Secure AI Framework) with Coalition for Secure AI (CoSAI) delivers industry-wide collaboration standards, focusing on supply chain security and model provenance verification.

• CISA's guidance for critical infrastructure addresses sector-specific requirements, particularly valuable for organizations managing essential services and national security applications.

Actionable non-deterministic AI defense implementation steps

1. Deploy retrieval-augmented generation (RAG) to reduce hallucinations by grounding responses in verified knowledge bases.

2. Implement runtime monitoring with continuous evaluation of model behavior, detecting anomalies and adversarial inputs in real-time through statistical analysis and behavioral baselines.

3. Configure output filtering using content validation layers that sanitize responses before delivery, preventing data leakage and inappropriate content generation.

4. Establish security guardrails through policy enforcement engines that maintain compliance boundaries and prevent unauthorized model behaviors.

5. Integrate risk assessment tools, like Snyk AppRisk, to systematically identify AI-related vulnerabilities across your development pipeline, providing visibility into model dependencies and security posture.

Non-deterministic GenAI incident response Framework

1. Model behavior analysis - Tracking prompt injection patterns

2. Training data contamination - Identifying poisoned datasets

3. Output monitoring - Detecting hallucinations and bias drift

Snyk Code for static analysis of AI application code and Snyk IaC for secure infrastructure deployment integrate seamlessly into CI/CD pipelines, enabling early vulnerability detection before production deployment.

Snyk AI Trust Platform creates defense-in-depth strategies essential for production AI systems. The key lies in selecting appropriate frameworks based on your organization's risk profile and implementing layered protections that address both technical and governance requirements.

Ready to explore beyond the traditional cybersecurity playbook? Dowload the AI TrustOps Ebook today.