Why Open-Source Foundation Models Are Security's Greatest Paradox

Open-source foundation models promise to democratize artificial intelligence, putting powerful AI capabilities into the hands of developers, researchers, and organizations worldwide. Yet they simultaneously create an attack surface so vast that even the brightest security minds struggle to contain it.

The xz Utils backdoor discovered in March 2024 exposed how a single compromised open-source library nearly brought down Linux infrastructure across millions of servers globally. Now imagine that threat scaling to AI models accessible to anyone with an internet connection.

The paradox is stark: openness drives innovation while simultaneously enabling exploitation. Understanding this tension has become essential for every organization deploying AI.

The security implications of open-source foundation models extend far beyond traditional software vulnerabilities. They encompass supply chain risks, adversarial misuse, data poisoning attacks, and the challenge of monitoring systems deployed across jurisdictions where creators have zero visibility into how models are being used or modified.

The Double-Edged Nature of Open Access

The appeal of open-source foundation models is undeniable. Organizations can inspect model weights, customize systems for specific use cases, deploy AI locally without relying on external APIs, and reduce dependence on proprietary vendors.

More actors may participate in AI research and development, lowering barriers to entry, promoting an open AI ecosystem, and reducing concentration of power in the industry.

Yet this same openness creates profound security challenges. Unlike closed models controlled through APIs, open-source models cannot be patched centrally. Once released, model weights exist freely available across hosting platforms like HuggingFace, GitHub, and countless mirrors.

If a security flaw is discovered, developers have no mechanism to prevent existing deployments from remaining vulnerable. The original creators cannot monitor how models are being modified, deployed, or misused in downstream applications.

Adversaries can leverage open models for their own ends without the resources required to design models from scratch. Open models may help competitors keep pace in AI development, influencing strategic competition, and malicious actors may deploy and fine-tune models for scams, disinformation, non-consensual deepfakes, and offensive cyber operations via social engineering.

This problem intensifies when attackers combine open models with other attack vectors. Large language models excel at analyzing code for vulnerabilities, crafting convincing phishing messages, and generating synthetic deepfake content. The same capabilities that help developers build better systems enable threat actors to operate at scale and efficiency previously impossible.

Supply Chain Attacks: The New Frontier of AI Risk

The software supply chain has become a battleground. The xz Utils incident exposed how a backdoor hidden in widely-used open-source software nearly compromised Linux infrastructure.

Recent attacks have targeted popular npm libraries like solana/web3.js, compromising cryptocurrency wallet private keys. Supply chain attacks are expected to increase in 2025 due to reliance on open-source libraries and sophisticated methods like phishing and social engineering.

Foundation models create new attack vectors within supply chains. An adversary could compromise a widely-used open-source AI library, inject malicious code into a fine-tuning framework, or poison training data used by downstream models. Organizations consuming these components often lack visibility into what they're actually deploying.

Organizations currently lack mature governance structures for their open-source estates, creating complications to adopting AI securely. Less than half of organizations take formal strategies before adopting open-source components, creating significant risk exposure and limiting ability to capture strategic value.

This governance gap proves particularly dangerous for AI systems where understanding model provenance, training data origins, and upstream dependencies becomes critical.

The challenge compounds when considering hidden dependencies. A foundation model might depend on multiple upstream libraries, any of which could be compromised. Organizations may unknowingly deploy vulnerable code chains stretching across dozens of projects maintained by different maintainers globally.

The Maintainer Crisis: Security's Weakest Link

The foundation of open-source security rests on an increasingly fragile premise. In 2024, Tidelift's results showed that 60% of open-source maintainers are unpaid. Most open-source systems operate under a dangerously fragile premise, often maintained by goodwill rather than mechanisms that align responsibility with usage, while a small number of organizations absorb the majority of infrastructure costs.

This unsustainable dynamic creates profound security risks. Unpaid maintainers lack resources for rigorous security testing, code review processes, and vulnerability response. They may lack expertise in secure development practices. They certainly lack bandwidth to implement sophisticated security monitoring. When attack occurs, response times suffer accordingly.

The situation worsens for AI models. Foundation models require specialized expertise to evaluate, test, and validate safely. The security requirements exceed what traditional open-source maintenance practices address. Yet most open-source AI projects operate under the same volunteer structures that struggle with basic security.

Misuse Risks: When Openness Enables Harm

Open-source foundation models enable capabilities previously restricted to well-resourced organizations. This democratization brings genuine benefits but also unleashes misuse potential at scale.

Threat actors can fine-tune open models for biological weapons design, crafting spear-phishing campaigns, generating non-consensual synthetic media, or automating complex cyber attacks.

The challenge lies in distinguishing legitimate research from misuse preparation. The same technical information useful for improving biosecurity safeguards could help malicious actors develop dangerous pathogens. The same model customization enabling privacy-preserving medical AI could enable creating indistinguishable deepfakes for fraud or political manipulation.

Developers of closed models can swiftly react to safety concerns by restricting access to compromised models through APIs. To mitigate risks with open models, developers and hosting communities like HuggingFace should adopt rigorous release and access management policies, following responsible guidelines to minimize safety risks while realizing open-source benefits.

Yet enforcement proves difficult. Posting responsible use guidelines on HuggingFace does not prevent someone from downloading model weights and deploying them for harmful purposes. Tracking dissemination and misuse across global networks remains technically challenging and resource-intensive.

Building Security Into Open-Source AI

Progress requires multi-layered approaches. Organizations deploying open-source models must establish formal governance structures with clear ownership, security evaluation processes, and supply chain visibility.

Organizations should establish open-source governance structures and implement Open Source Program Offices (OSPOs) to manage compliance, security, and contribution workflows, recognizing that most lack the governance frameworks and security practices needed for mission-critical deployments.

At the ecosystem level, model providers and developers should implement secure channels for external stakeholders to report safety incidents, enable internal teams to responsibly report incidents with whistleblower protection, and contribute anonymized data to collaborative incident tracking initiatives to identify systemic issues.

Funding mechanisms must shift. Open-source security cannot depend on unpaid volunteers. Whether through government investment, corporate sponsorship, or sustainability models that fairly compensate maintainers, resources must align with actual dependency and security requirements.

Pre-release evaluation standards need development. Risk thresholds that may weigh against open release of certain models are not yet articulated, though developing shared risk thresholds for frontier models has been identified as a priority for industry and government. These thresholds should guide decisions about staged rollouts versus immediate public release.

The Path Forward: Balancing Innovation and Security

The answer is not to restrict open-source AI. The innovation benefits, research acceleration, and power distribution advantages prove too valuable to sacrifice. Instead, stakeholders must implement thoughtful safeguards that preserve openness while raising security barriers sufficiently to deter casual misuse.

This requires orchestration across model developers, hosting platforms, organizations deploying models, and security researchers. It demands that organizations shift from underinvesting in governance and security to treating these as foundational requirements. It means funding open-source maintainers adequately so security becomes a routine practice rather than aspirational goal.

The remarkable irony of open-source foundation models is this: their greatest strength creates their greatest vulnerability. But that paradox is not unmanageable. It simply demands recognition that openness, properly secured, remains the path forward.

The alternative of restricting access creates a different set of risks while sacrificing innovation that benefits humanity. The challenge ahead is executing thoughtfully, with clear-eyed assessment of tradeoffs and sustained commitment to building resilience into systems designed for openness.

Fast Facts: Open-Source Foundation Models Explained

What are open-source foundation models and why are they security-critical?

Open-source foundation models are AI systems with publicly available weights that anyone can download, inspect, and customize. They're security-critical because they're deployed across organizations globally without central control. Unlike closed models managed through APIs, once released, developers cannot patch vulnerabilities or prevent misuse of open-source foundation models across all deployments.

How do attackers exploit open-source foundation models specifically?

Threat actors use open-source foundation models to analyze code for vulnerabilities, craft convincing phishing campaigns, generate deepfakes, or design biological weapons. They also compromise hosting platforms and supply chains to inject malicious code into widely-used AI libraries, poisoning models that downstream organizations unknowingly deploy without visibility.

What's preventing organizations from securely adopting open-source foundation models?

Organizations lack mature governance frameworks, formal security evaluation processes, and supply chain visibility for open-source deployments. Most skip formal security assessments before adoption, and nearly 60% of open-source maintainers are unpaid, creating resource constraints that limit rigorous security testing and vulnerability response capabilities.