The AI Supply Chain: Securing the Future of Agentic Systems

by

Author: Denis Avetisyan

As large language models become increasingly integrated into autonomous AI agents, ensuring their security requires a fundamental shift towards proactive supply chain oversight.

This review assesses the emerging risks to model provenance and trust, advocating for verifiable attestation and enforceable governance throughout the AI lifecycle.

While large language models (LLMs) promise revolutionary advances in cybersecurity, their increasing deployment also introduces novel systemic risks. This is explored in ‘LLM Scalability Risk for Agentic-AI and Model Supply Chain Security’, which integrates offensive and defensive perspectives on GenAI-driven threats to demonstrate the necessity for proactive security measures. The paper introduces the LLM Scalability Risk Index (LSRI) and a model supply-chain framework to establish verifiable trust throughout the AI lifecycle, arguing that scalable defense requires a shift from reactive threat detection to verifiable provenance. Can robust governance and enforceable trust mechanisms secure the rapidly evolving landscape of agentic AI and its critical role in future cybersecurity?

The Expanding Threat Vector: LLMs and Cybersecurity’s New Calculus

The integration of Large Language Models (LLMs) represents a pivotal shift in cybersecurity, simultaneously bolstering defenses and creating unprecedented vulnerabilities. These models excel at automating threat detection through analysis of vast datasets, identifying anomalous patterns indicative of malicious activity, and even predicting potential attacks before they materialize. However, this same power fuels sophisticated new attack vectors; LLMs can generate highly convincing phishing emails, craft polymorphic malware that evades signature-based detection, and automate the discovery of software vulnerabilities at an alarming rate. This duality necessitates a fundamental rethinking of security protocols, moving beyond reactive measures to embrace proactive, AI-driven strategies capable of countering threats originating from – and defended by – these increasingly intelligent systems. The landscape is no longer simply about preventing known attacks, but about anticipating and neutralizing those conceived and executed with the aid of advanced artificial intelligence.

The advent of Large Language Models has instigated a period of ‘Cyber Threat Inflation’, dramatically lowering the barrier to entry for malicious actors seeking to orchestrate complex cyberattacks. Previously, crafting convincing phishing emails, generating polymorphic malware, or automating vulnerability exploitation required significant expertise and resources. Now, LLMs empower individuals with limited technical skills to produce highly realistic and persuasive malicious content at scale, and even to autonomously discover and exploit system weaknesses. This accessibility fundamentally alters the threat landscape, rendering traditional, signature-based security measures increasingly ineffective. Consequently, a paradigm shift is crucial, moving beyond reactive defenses towards proactive, AI-driven security strategies capable of anticipating, detecting, and mitigating LLM-powered attacks in real-time.

Conventional cybersecurity measures, built upon signature-based detection and rule-based systems, are increasingly challenged by the dynamic and adaptive nature of threats generated by Large Language Models. These models enable the creation of polymorphic malware and highly convincing phishing campaigns that easily evade established defenses. The sheer volume and sophistication of LLM-powered attacks necessitate a shift towards proactive security strategies; systems must now anticipate and neutralize threats before they manifest. This requires the implementation of AI-driven threat detection, employing machine learning algorithms to analyze patterns, predict malicious activity, and automate rapid response – a fundamental reimagining of security from reactive to preemptive, and from signature-based to behavioral analysis.

Establishing a Verifiable Root of Trust: The LLM Supply Chain

A Model Supply Chain Framework establishes a verifiable root of trust for Large Language Models (LLMs) by tracking all components and processes throughout the LLM lifecycle. This framework extends beyond traditional software supply chain security to encompass data sources, training procedures, model architecture, fine-tuning parameters, and deployment infrastructure. Establishing this framework requires detailed documentation and automated verification at each stage, including data provenance tracking, model versioning, and continuous monitoring for drift or malicious modifications. A robust framework facilitates auditing, vulnerability management, and responsible AI governance, ensuring the integrity and reliability of LLM-powered applications from initial development through ongoing operation and updates.

The AI Model Bill of Materials (AIBOM) is a structured, machine-readable inventory detailing all components contributing to an LLM. This includes the training dataset(s) used – specifying sources, versions, and preprocessing steps – as well as the model architecture, hyperparameters, and training procedures. AIBOMs also document the software dependencies, such as libraries and frameworks, and hardware utilized during training and inference. Crucially, the AIBOM tracks changes made throughout the LLM’s lifecycle, establishing a complete audit trail for reproducibility, vulnerability management, and compliance verification. The standardized format facilitates automated analysis and integration with existing software supply chain security tools.

The LLM Scalability Risk Index (LSRI) provides a methodology for evaluating operational risks specifically associated with scaling Large Language Model deployments. Traditional software supply chain security measures are insufficient for LLMs due to their unique characteristics, including high computational demands, reliance on external data sources, and potential for emergent behaviors. LSRI stress-tests assess an LLM’s performance under varying loads, input types, and adversarial conditions, identifying vulnerabilities related to resource exhaustion, latency spikes, and unexpected outputs. Organizations can utilize LSRI results to implement mitigation strategies, such as optimized infrastructure provisioning, input validation techniques, and runtime monitoring, thereby proactively addressing risks inherent in scaling LLM applications beyond typical software deployments.

Fortifying LLM Resilience: A Defense-in-Depth Posture

Adversarial robustness in Large Language Models (LLMs) refers to the ability of the model to maintain consistent and accurate performance when presented with intentionally crafted, malicious inputs – often referred to as ‘adversarial examples’. These inputs are specifically designed to exploit vulnerabilities in the LLM’s architecture or training data, potentially causing incorrect outputs, unexpected behavior, or even security breaches. Achieving adversarial robustness is critical because standard LLMs can be highly susceptible to even subtle perturbations in input, leading to unpredictable failures. This is particularly concerning in applications where reliability and security are paramount, such as healthcare, finance, and autonomous systems. Mitigation strategies focus on techniques like adversarial training, input validation, and robust model architectures to improve resilience against these attacks.

Combining static and dynamic analysis offers a robust methodology for identifying vulnerabilities in Large Language Models (LLMs). Static analysis examines the LLM’s code and architecture without execution, identifying potential weaknesses like coding errors, insecure configurations, or problematic dependencies. This process includes examining the model’s weights and biases for anomalies. Dynamic analysis, conversely, involves testing the LLM with various inputs while it’s running to observe its behavior and detect runtime errors, unexpected outputs, or performance bottlenecks. Combining these approaches provides comprehensive coverage; static analysis proactively identifies potential issues, while dynamic analysis validates those findings and uncovers vulnerabilities that only manifest during execution, such as prompt injection attacks or denial-of-service conditions. This dual approach minimizes the risk of overlooking critical weaknesses in the LLM’s implementation and operation.

Explainable AI (XAI) techniques applied to Large Language Models (LLMs) provide insights into the model’s decision-making processes, allowing developers to identify and address potentially exploitable logic. Concurrently, model watermarking embeds subtle, statistically detectable patterns within the model’s parameters or generated outputs without affecting performance. These watermarks serve as a form of digital signature, enabling attribution of generated content and detection of unauthorized model copies or malicious modifications. The combined implementation of XAI and model watermarking constitutes a defense-in-depth strategy that reduces the vulnerability surface by increasing observability and accountability, thereby assisting in the identification and mitigation of adversarial attacks and intellectual property theft compared to standard LLM deployment methods.

Expanding the Horizon: Federated Learning and the Future of Secure AI

Federated learning presents a transformative approach to training large language models by shifting the paradigm from centralized data collection to distributed collaboration. Instead of requiring sensitive data to be pooled in a single location, this technique enables model training directly on decentralized devices – such as smartphones or organizational servers – while keeping the raw data localized. The process involves sharing only model updates, rather than the data itself, significantly bolstering privacy and security. This not only mitigates risks associated with data breaches and centralized vulnerabilities but also broadens access to training data, potentially leading to more robust and representative LLMs. By overcoming the limitations of data silos and privacy concerns, federated learning unlocks the potential of previously inaccessible datasets, fostering innovation and democratizing access to advanced AI capabilities.

A robust security posture in the age of large language models demands a shift towards preventative measures, primarily through meticulous code review and the seamless integration of security into DevOps automation – often termed DevSecOps. Rather than reacting to vulnerabilities post-deployment, this approach emphasizes identifying and rectifying flaws earlier in the development lifecycle. Automated security testing, integrated directly into the continuous integration and continuous delivery pipelines, allows for rapid assessment and remediation of potential weaknesses. This proactive stance isn’t merely about fixing bugs; it’s about building resilience, fostering a security-conscious culture among developers, and ensuring that security considerations are fundamental to every stage of the software development process, ultimately enabling a more adaptable and secure system capable of withstanding evolving threats.

Effective AI governance stands as a critical necessity for harnessing the potential of large language models while simultaneously addressing the risks posed by technologies capable of both beneficial and harmful applications – often termed ‘Dual-Use AI’. A newly proposed framework directly confronts a key challenge within this governance: the performance impact of rigorous cryptographic verification. Recognizing that extensive verification processes can significantly slow down LLM operations, the framework prioritizes minimizing this overhead. By streamlining verification protocols, it seeks to balance robust security measures with sustained performance, enabling wider adoption of responsibly governed AI systems without compromising speed or efficiency. This approach acknowledges that practical implementation of AI governance requires not only ethical guidelines, but also technical solutions that facilitate seamless integration into existing infrastructure.

The pursuit of scalable LLMs, as detailed in the document, necessitates a rigorous foundation akin to mathematical proof. The paper rightly emphasizes verifiable model provenance and supply chain security, moving beyond simply demonstrating functionality to establishing demonstrable correctness. This echoes Bertrand Russell’s observation: “The whole problem of philosophy is to account for the fact that anything exists.” Just as Russell sought fundamental truths, this work posits that secure AI isn’t about patching vulnerabilities, but about building systems founded on verifiable origins and trustworthy components-a logical necessity for dependable operation and mitigating dual-use risks.

What’s Next?

The preceding analysis suggests that current approaches to large language model (LLM) security are, at best, treating symptoms. The field fixates on detection of malicious outputs, a fundamentally reactive posture. A truly robust solution demands a shift toward deterministic provenance. If a model’s behavior cannot be traced to its originating data and training process with absolute certainty, its deployment represents an unacceptable risk. The question, then, is not simply whether an LLM can be secured, but whether it can be proven secure.

The pursuit of verifiable model attestation, however, reveals a thorny problem. Existing methods for establishing trust – digital signatures, supply chain tracking – assume a level of control over the entire lifecycle that is demonstrably absent in the current fragmented ecosystem. The open-source nature of many foundational models, while fostering innovation, introduces further complications. The very notion of “ownership” becomes blurred, making enforceable trust mechanisms exceptionally difficult to implement. A provably secure LLM, it seems, requires a level of centralized control that may be politically or practically untenable.

Future research must therefore focus on developing cryptographic techniques that allow for verifiable computation on distributed datasets, enabling a form of “proof-of-origin” for model weights. Furthermore, the field needs a rigorous formalism for defining and quantifying the risks associated with dual-use AI, moving beyond qualitative assessments toward mathematically grounded metrics. Only then can the industry begin to address the inherent fragility of LLM-powered systems with something approaching scientific rigor.

Original article: https://arxiv.org/pdf/2602.19021.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-24 19:03