Supply Chain Vulnerabilities in AI: How Compromised Components Infiltrate Your LLM Applications (OWASP LLM Top 10 #3)
Every AI application your organization uses is built from components you did not create: pre-trained models, fine-tuning adapters, third-party plugins, open-source libraries, and training datasets sourced from across the internet. Supply Chain Vulnerabilities, ranked #3 in the 2025 OWASP Top 10 for LLM Applications, represent the risk that any of these components could be compromised before they ever reach your systems. This guide explains how supply chain attacks against AI applications work, why they are uniquely difficult to detect, and what your organization can do to verify the integrity of the AI tools it depends on.
When your organization adopts an AI tool, you are not just adopting the tool itself. You are inheriting every decision, every data source, every library, and every model weight that went into building it. A single chatbot might depend on a base language model trained by one company, fine-tuned using adapters from an open-source repository, connected to plugins built by independent developers, and grounded in datasets curated by yet another organization entirely. Each of these components represents a link in the supply chain, and each link is a potential point of compromise.
Supply Chain Vulnerabilities moved from #5 in the original OWASP Top 10 for LLM Applications to #3 in the 2025 edition. This jump reflects how rapidly the attack surface has expanded as organizations adopt more complex AI architectures. The ecosystem of AI components has exploded: Hugging Face now hosts over one million machine learning models, PyPI and npm serve millions of AI-related packages, and the number of third-party plugins and integrations available for major AI platforms grows daily. With this expansion comes an equally rapid growth in opportunities for malicious actors to insert compromised components into the supply chain.
This is the third article in our series covering every vulnerability in the OWASP Top 10 for LLM Applications. The first article on prompt injection explored how attackers manipulate AI through crafted inputs, and the second article on sensitive information disclosure examined how AI systems leak confidential data. Supply chain vulnerabilities are different in a critical way: the compromise happens before the AI system is even deployed. By the time your organization installs, connects, or fine-tunes with a compromised component, the damage is already embedded in the foundation of your system.
For nonprofits, this risk is compounded by several factors. Limited security budgets mean fewer resources for vetting third-party components. Pressure to adopt AI quickly can lead to shortcuts in due diligence. And the nonprofit sector's reliance on free and open-source tools, while financially necessary, also increases exposure to components that may not have undergone rigorous security review. In this article, we will examine exactly how supply chain attacks work against AI applications, identify the components most frequently targeted, explain why traditional software security tools miss these threats, and provide a practical defense framework that organizations of any size can implement.
What Supply Chain Vulnerabilities Actually Are
In traditional software development, a supply chain vulnerability occurs when an attacker compromises a component that other software depends on. The SolarWinds attack of 2020, in which hackers inserted malicious code into a trusted software update that was then distributed to thousands of organizations, is the most well-known example. The same principle applies to AI applications, but the attack surface is broader and the components involved are fundamentally different from traditional software dependencies.
AI supply chains include everything from the model weights themselves (the mathematical parameters that determine how the AI behaves) to the training datasets that shaped those weights, the fine-tuning adapters that customize models for specific tasks, the libraries and frameworks that process data during inference, the plugins and integrations that extend the AI's capabilities, and the infrastructure services that host and deliver these components. A compromise at any point in this chain can propagate through to the final deployed application, often without leaving any obvious trace.
What makes AI supply chain vulnerabilities particularly dangerous is that many of the attack vectors do not look like traditional security incidents. A poisoned training dataset does not trigger antivirus software. A backdoored model adapter does not generate suspicious network traffic. A malicious plugin that activates only under specific input conditions can pass standard testing with flying colors. The attacks are designed to be invisible until the attacker chooses to exploit them, and the traditional security tools that organizations rely on were never designed to detect compromises embedded in model weights or serialized Python objects.
Traditional Software Supply Chain vs. AI Supply Chain
Traditional Software Supply Chain
- •Dependencies are code libraries with reviewable source code
- •Vulnerabilities are cataloged in CVE databases
- •Static analysis and code scanning can detect known issues
- •Package managers provide version pinning and integrity checks
AI Supply Chain
- •Dependencies include opaque model weights, datasets, and serialized objects
- •No standard vulnerability database exists for AI model compromises
- •Traditional code scanners cannot inspect model weights or training data
- •Model provenance and integrity verification are still immature
Understanding the distinction between traditional and AI supply chains is essential because it reveals why organizations cannot simply extend their existing software security practices to cover AI deployments. The components are different, the attack vectors are different, the detection methods are different, and the remediation strategies are different. Organizations that treat AI security as an extension of software security are likely to miss the most significant threats.
How Supply Chain Attacks Work in Practice
Supply chain attacks against AI systems take many forms, targeting different components at different stages of the development and deployment lifecycle. Understanding each attack pattern is essential for building defenses that address the full range of threats.
Compromised Pre-trained Models and Adapters
Malicious actors upload backdoored models to public repositories, disguising them as legitimate community contributions
Open-source model repositories like Hugging Face have become the default source for pre-trained models and fine-tuning adapters. Security researchers have identified hundreds of malicious models on these platforms, many using Python's Pickle serialization format, which allows arbitrary code execution when a model file is loaded. In early 2025, researchers at ReversingLabs discovered malicious machine learning models on Hugging Face that evaded the platform's security scanning by exploiting gaps in automated detection. Separately, the NullifAI campaign specifically abused the Pickle format to distribute malware through models that appeared completely legitimate.
LoRA (Low-Rank Adaptation) adapters present a particularly insidious risk. These small files are designed to modify a base model's behavior for specific tasks, and they are increasingly popular because they are cheap to create and easy to share. But a malicious LoRA adapter can introduce backdoors that activate only under specific input conditions, causing the model to produce dangerous outputs while behaving normally during standard testing. For organizations that download community adapters to customize AI for their specific use case, this represents a direct threat that is nearly impossible to detect without specialized adversarial testing.
- JFrog researchers identified over 100 malicious AI/ML models on Hugging Face, confirming the threat has moved beyond theory into real-world exploitation
- Pickle-based model files can execute arbitrary Python code when loaded, enabling attackers to compromise the host system entirely
- Backdoored adapters can pass standard functionality tests while containing hidden triggers that activate under attacker-controlled conditions
Malicious Packages and Dependency Attacks
Attackers exploit AI-related package ecosystems to distribute compromised code through trusted distribution channels
The AI ecosystem relies heavily on package managers like PyPI (Python), npm (JavaScript), and conda for libraries that handle everything from data preprocessing to model inference. Attackers have exploited these ecosystems through several techniques. Typosquatting involves uploading malicious packages with names that closely resemble popular AI libraries, counting on developers to make typographical errors during installation. Dependency confusion exploits how package managers resolve naming conflicts between internal and public repositories, tricking build systems into installing a malicious public package instead of the intended internal one. In one notable incident, a malicious package called "torchtriton" was uploaded to PyPI, mimicking a legitimate PyTorch dependency, and infiltrated thousands of systems within hours.
A newer and more concerning threat involves what researchers call "slopsquatting," which exploits the fact that AI coding assistants frequently hallucinate package names that do not exist. Researchers found that roughly 20% of AI-generated code references nonexistent packages. Attackers have begun registering these hallucinated package names on public repositories and populating them with malicious code, effectively turning AI's unreliability into an attack vector. When a developer trusts AI-generated code and installs the suggested package without verifying it, they unwittingly install malware.
- Malicious package uploads to open-source repositories have increased significantly, with AI-targeted packages representing a growing share
- Slopsquatting exploits AI hallucinations: attackers register fake package names that AI assistants commonly suggest in generated code
- A single compromised library can cascade through the entire AI application stack, affecting model loading, data processing, and inference
Plugin and Integration Compromises
Third-party plugins and API integrations extend AI capabilities but also introduce unvetted code into the execution pipeline
Modern AI applications increasingly rely on plugins and integrations to extend their capabilities: connecting to databases, retrieving web content, processing documents, sending emails, or interacting with other enterprise systems. Each plugin operates as executable code within the AI application's trust boundary, meaning a compromised plugin can access whatever the AI application itself can access. In March 2024, researchers at Salt Security exposed a vulnerability in the ChatGPT ecosystem that allowed malicious plugins to be installed on users' accounts and take over third-party accounts connected through the plugin architecture.
The Model Context Protocol (MCP) and similar standards for connecting AI systems to external tools and data sources have created a new category of supply chain risk. When an organization connects an MCP server to its AI assistant, it is granting that server access to the AI's context and, potentially, to the user's data and system capabilities. A compromised MCP server can intercept sensitive data, manipulate AI responses, or use the AI's permissions to take unauthorized actions. The security of your AI application is only as strong as the weakest integration in its chain.
- Plugins and integrations execute within the AI application's trust boundary, giving compromised components broad access
- Third-party AI tool connections can intercept data flowing between your systems and the AI model
- Plugin marketplaces often lack rigorous security review, relying on community reporting to identify malicious submissions
Training Data and Dataset Poisoning
Compromised datasets inject biased, incorrect, or malicious patterns into the AI's learned behavior
AI models are only as trustworthy as the data they were trained on, and most organizations have limited visibility into the training data of the models they use. Publicly available datasets used for fine-tuning can be manipulated to introduce biases, embed harmful behaviors, or create backdoor triggers that cause the model to behave maliciously under specific conditions. Unlike code-based attacks that can theoretically be found through inspection, data poisoning is extremely difficult to detect because the "malicious" content is statistical patterns distributed across millions of training examples.
This risk is especially relevant for organizations that use retrieval-augmented generation (RAG) systems, where the AI retrieves information from a knowledge base to ground its responses. If the knowledge base contains poisoned documents, whether through external sourcing or insider manipulation, the AI will incorporate that poisoned information into its outputs with the same confidence as legitimate content. The user has no way to distinguish between a response grounded in accurate data and one grounded in deliberately corrupted data.
- Data poisoning can be targeted to affect specific topics, demographics, or organizational contexts while leaving general model performance intact
- Organizations rarely have access to or visibility into the full training data of the models they deploy
- RAG knowledge bases sourced from unverified external content are particularly vulnerable to document-level poisoning
AI Coding Assistant Vulnerabilities
Compromised development tools can inject vulnerabilities into the AI applications your team builds
A new and rapidly evolving supply chain risk comes from AI coding assistants themselves. Organizations increasingly use tools like GitHub Copilot, Cursor, and similar AI-powered development environments to build their AI applications. In 2025, researchers discovered CVE-2025-53773 in Microsoft Copilot and Visual Studio, a vulnerability that allowed attackers to inject instructions through malicious source code files. The exploit automatically modified developer settings and executed system commands without consent, and could propagate across repositories as a self-replicating infection.
This creates a cascading supply chain risk: the tools used to build AI applications can themselves be vectors for injecting vulnerabilities into the applications they help create. For organizations using AI-assisted development approaches, this means that the security of their AI deployments depends not only on the components they integrate but also on the integrity of the development tools they use to build and configure those deployments.
- AI coding assistants can be manipulated through prompt injection embedded in source code repositories
- Compromised development tools can generate insecure code or introduce vulnerabilities in AI-generated code that developers may not catch during review
- SockPuppet attacks use AI-generated developer profiles that contribute legitimate code for months before injecting backdoors
Why Traditional Security Tools Fail
Organizations that have invested in software composition analysis (SCA) tools, dependency scanners, and vulnerability databases may assume they are covered for AI supply chain risks. They are not. Traditional tools were designed to detect known vulnerabilities in code libraries, not to identify compromises embedded in model weights, serialized objects, or training datasets. This creates a dangerous gap between perceived security and actual exposure.
Consider how a standard software composition analysis tool works: it scans your project's dependencies, cross-references them against a database of known vulnerabilities (like the National Vulnerability Database), and flags any matches. This process is effective for traditional software libraries where vulnerabilities are cataloged with CVE identifiers. But AI model files are not code libraries. A model weight file is a binary blob of mathematical parameters, typically hundreds of megabytes to tens of gigabytes in size. No CVE database catalogs compromised model weights. No static analysis tool can inspect billions of floating-point parameters to determine whether they encode a backdoor trigger. The compromise is mathematical, not syntactic, and it is fundamentally opaque to traditional inspection methods.
The serialization problem adds another layer of risk. Many model formats, particularly those using Python's Pickle protocol, allow arbitrary code to execute when the file is deserialized. While safer alternatives like Safetensors have been developed, the Pickle format remains widely used. Even Hugging Face's Safetensors conversion service has been shown to have vulnerabilities that hackers could exploit. Traditional antivirus tools may catch some known malware patterns in serialized files, but sophisticated attackers can obfuscate their payloads to evade signature-based detection.
This is why a specialized approach to AI application security is essential. Traditional security tools are a necessary foundation, but they are insufficient for the AI-specific attack surface. Organizations need to supplement their existing security stack with tools and practices specifically designed for AI component verification, model integrity testing, and behavioral analysis that can detect compromises that traditional tools miss entirely.
Who Is at Risk
Any organization using AI is exposed to supply chain vulnerabilities, but the level of risk varies based on how deeply integrated AI components are into your operations and how many third-party components your AI deployments depend on.
Organizations Using Open-Source Models
Downloading pre-trained models or fine-tuning adapters from public repositories like Hugging Face, GitHub, or community model hubs. Every model file is a potential vector for code execution or embedded backdoors, and most organizations lack the tools to verify model integrity before deployment.
Teams Building Custom AI Applications
Development teams that assemble AI applications from multiple third-party components, including model APIs, data processing libraries, vector databases, and integration plugins. Each dependency adds supply chain risk, and the interactions between components create additional attack surface.
Organizations Using AI SaaS Products
Even organizations that use AI exclusively through SaaS vendors inherit supply chain risk. If your vendor's model, training data, or infrastructure is compromised, the compromise flows through to your organization's data and operations. Vendor due diligence must extend to their AI supply chain practices.
Organizations Using RAG and Knowledge Bases
Systems that ground AI responses in external data sources, scraped web content, or third-party document collections. Poisoned documents in the knowledge base can manipulate AI outputs without any compromise to the model itself, making this one of the easiest supply chain attacks to execute.
Why Nonprofits Face Elevated Supply Chain Risk
Nonprofit organizations face a particular combination of factors that amplify supply chain risk. Limited budgets often mean relying on free, open-source AI components that may lack the security investment of commercial alternatives. Smaller technical teams may not have the specialized knowledge to evaluate AI model integrity or detect subtle behavioral compromises. The pressure to demonstrate innovation to funders and boards can accelerate adoption timelines beyond what security due diligence can support.
At the same time, the data that nonprofits work with, including client records for social services, health data for community health organizations, educational records for youth-serving organizations, and donor financial information, is exactly the kind of sensitive information that supply chain attackers aim to exfiltrate. A compromised AI component in a nonprofit's case management system could silently transmit client data to an attacker for months before detection, putting vulnerable populations at direct risk.
Understanding your organization's supply chain exposure is the first step toward managing it. A professional AI security assessment can map your AI dependencies, evaluate the integrity of the components you rely on, and identify the highest-risk links in your chain.
Defense Strategies: A Layered Approach
Defending against supply chain vulnerabilities requires a layered strategy that addresses each stage of the AI component lifecycle: sourcing, verification, deployment, and monitoring. No single defense is sufficient, but together these layers create a framework that significantly reduces your organization's exposure to compromised components.
Layer 1: Component Sourcing and Vendor Due Diligence
Establish trusted sources and evaluate the security posture of every component provider before adoption
The first line of defense is controlling where your AI components come from. This applies to models, adapters, libraries, plugins, datasets, and SaaS services. Every component should be sourced from a provider whose identity you can verify, whose security practices you can evaluate, and whose track record you can assess. This is not about avoiding open-source tools entirely. It is about being deliberate about which sources you trust and applying consistent evaluation criteria.
For nonprofit organizations, vendor due diligence should extend beyond traditional IT questionnaires. When evaluating an AI vendor or component, ask about their data privacy practices, how they verify the integrity of their training data, what security testing they perform on their models, and whether they maintain a Software Bill of Materials (SBOM) for their AI components. Organizations that are subject to AI regulatory requirements should ensure their vendors can demonstrate compliance.
- Only use models from verifiable sources with documented provenance, including the training data sources, training methodology, and any fine-tuning applied
- Evaluate AI vendors for security practices specific to AI, not just general IT security certifications like SOC 2
- Require vendors to document their own supply chain dependencies, including which models, datasets, and libraries they depend on
- Avoid downloading models or adapters from anonymous or unverified accounts on public repositories
Layer 2: Integrity Verification and Component Validation
Verify that every component is authentic and unmodified before it enters your deployment pipeline
Once you have identified trusted sources, the next layer is verifying that the components you receive are actually what they claim to be. This means implementing cryptographic integrity checks at every point where a component enters your systems. For model files, use digital signatures and file hashes provided by the model publisher. For software libraries, use package managers with lockfile support and enable signature verification. For datasets, maintain checksums and audit trails that document every modification.
Maintaining a Software Bill of Materials (SBOM) for your AI applications is critical. An SBOM is an inventory of every component in your AI deployment: the base model and its version, every adapter and plugin, every library and its version, every dataset and its source, and every API integration. This inventory enables rapid response when a vulnerability is disclosed in any component. Without an SBOM, you cannot answer the most basic supply chain security question: "Are we affected by this newly discovered compromise?"
- Verify model file integrity using cryptographic hashes and digital signatures before loading into production
- Maintain an up-to-date SBOM that includes all AI-specific components: models, adapters, datasets, plugins, and libraries
- Prefer safer model serialization formats like Safetensors over Pickle when available
- Pin all dependency versions and use lockfiles to prevent unexpected updates from introducing compromised components
Layer 3: Isolation, Sandboxing, and Staged Deployment
Test all external components in isolated environments before allowing them into production systems
Even verified components should not be deployed directly to production. Every external model, adapter, plugin, and library should pass through an isolated testing environment where it can be evaluated for unexpected behavior without putting production data or systems at risk. This is the same principle that software engineering teams apply to staging environments, extended to cover the unique testing requirements of AI components.
In the isolated environment, conduct functional testing to verify the component behaves as expected, adversarial testing to probe for hidden triggers and backdoors, and behavioral analysis to identify outputs that deviate from expected patterns. For model files, this includes testing with inputs designed to activate common backdoor triggers. For plugins and integrations, this includes monitoring network traffic and file system access to detect unauthorized communication or data exfiltration. Only promote components to production after they pass all security and behavioral tests.
- Run all external AI components in sandboxed environments that restrict network access, file system access, and system calls
- Conduct adversarial testing specifically designed to trigger hidden backdoors in models and adapters
- Monitor all network traffic from new components to detect unauthorized data exfiltration or command-and-control communication
- Implement staged rollouts that deploy new components to a small subset of users before broad production release
Layer 4: Continuous Monitoring and Incident Response
Monitor deployed components for behavioral changes and maintain readiness to respond to newly discovered compromises
Supply chain compromises may not be detectable at the time of deployment. New vulnerabilities are disclosed regularly, and sophisticated backdoors may only activate under conditions that testing did not cover. Continuous monitoring bridges this gap by tracking the behavior of deployed AI components over time and alerting on deviations from expected patterns. This includes monitoring model outputs for quality degradation or unexpected content, tracking plugin and integration behavior for unauthorized network communication, and watching dependency advisories for newly disclosed vulnerabilities in components your applications depend on.
Equally important is having an incident response plan that addresses AI supply chain compromises specifically. When a compromised component is identified, your organization needs to know how to isolate the affected system, determine the scope of impact, notify affected stakeholders, and replace the compromised component with a verified alternative. This plan should be tested periodically, not just documented. Organizations with a zero-trust security posture are better positioned to contain supply chain incidents because their architecture already assumes that any component could be compromised.
- Implement automated monitoring for behavioral anomalies in deployed AI models and integrations
- Subscribe to security advisories for all AI components in your SBOM and respond promptly to new disclosures
- Maintain an AI-specific incident response plan that covers component isolation, impact assessment, and stakeholder notification
- Regularly update and patch all AI dependencies, treating model and adapter updates with the same urgency as software patches
Common Mistakes Organizations Make
Even organizations that take AI security seriously can fall into common traps that leave them exposed to supply chain attacks. These mistakes often stem from applying traditional software security thinking to a fundamentally different type of risk.
Treating Model Downloads Like Library Installs
Many organizations apply the same casual approach to downloading AI models that they use for installing npm or pip packages: find a popular one, check the download count, and install it. But model files carry fundamentally different risks than code libraries. A code library's behavior is deterministic and inspectable. A model file is an opaque binary whose behavior is probabilistic and cannot be fully predicted from inspection alone. Organizations that download models based solely on popularity metrics or community ratings are trusting the crowd to catch compromises that require specialized adversarial testing to detect.
Assuming Vendor Security Extends to Their AI Components
An AI vendor may have excellent traditional security certifications, SOC 2, ISO 27001, strong encryption practices, and robust access controls, while still having significant gaps in their AI supply chain security. These certifications do not evaluate whether the vendor verifies the integrity of the models they deploy, audits their training data sources, or tests for backdoors in fine-tuning adapters. Organizations that rely on vendor certifications as proof of AI supply chain security are protecting against a different set of threats than the ones that actually target AI components.
Neglecting the Data Supply Chain
Organizations often focus their supply chain security efforts on models and code while neglecting the data that powers their AI systems. But for many AI deployments, the data supply chain is the most vulnerable link. Training datasets sourced from the internet, RAG knowledge bases populated from external content, and benchmark datasets used for evaluation can all be poisoned. An organization that rigorously verifies its model integrity but carelessly ingests unvetted data into its RAG system has left the back door open while securing the front.
Skipping Behavioral Testing After Component Updates
When AI components are updated, whether a model version bump, a library patch, or a plugin update, many organizations deploy the update without re-running behavioral testing. They assume that if the previous version was safe, the update is safe too. This assumption ignores the reality that updates are a primary vector for supply chain attacks. An attacker who compromises a component maintainer's account can push a malicious update through the same trusted channel that delivered the original safe version. Every update, no matter how minor, should undergo the same verification and testing pipeline as the initial deployment.
What a Professional Assessment Covers
A comprehensive AI Application Security assessment evaluates your organization's supply chain exposure systematically, examining every layer from component sourcing to runtime monitoring. Here is what a thorough assessment examines.
Component Inventory and Provenance Mapping
Identifying every AI component in your deployment, including models, adapters, plugins, libraries, datasets, and API integrations. Tracing each component's provenance back to its source and evaluating whether that source meets acceptable trust criteria. Documenting dependencies between components to understand how a compromise in one could cascade through the system.
Model Integrity and Backdoor Testing
Testing model files for embedded malicious code, particularly in serialization formats known to be vulnerable. Probing models with adversarial inputs designed to trigger common backdoor patterns. Evaluating whether model behavior matches the documented specifications and whether hidden functionalities can be activated under specific input conditions.
Dependency and Library Audit
Scanning all software dependencies for known vulnerabilities, verifying package integrity against published hashes, checking for typosquatting or dependency confusion risks, and evaluating whether your dependency management practices, including lockfiles, version pinning, and update policies, are sufficient to prevent supply chain injection.
Data Pipeline and Knowledge Base Review
Evaluating the sources, validation processes, and integrity controls for all data that feeds into your AI systems. This includes training data, fine-tuning datasets, RAG knowledge bases, and any external data sources. Assessing whether your data pipelines can detect and reject poisoned content before it influences model behavior.
Plugin and Integration Security Review
Testing all third-party plugins, API integrations, and tool connections for unauthorized data access, privilege escalation, and data exfiltration. Evaluating the permission model that governs what each integration can access and whether the principle of least privilege is enforced across all connections.
Vendor AI Security Posture Evaluation
Assessing the AI-specific security practices of your vendors, including their model provenance documentation, training data governance, component testing procedures, and incident response capabilities. Identifying gaps between traditional vendor security certifications and AI-specific security requirements.
The Value of Supply Chain Visibility
Most organizations underestimate the number of AI components they depend on because they lack visibility into their own supply chain. A professional assessment provides this visibility by mapping every component, evaluating its provenance, testing its integrity, and documenting the trust relationships that connect your organization to the broader AI ecosystem. This inventory becomes the foundation for ongoing supply chain risk management: you cannot protect what you cannot see.
For organizations that handle sensitive data, including client records, financial information, or data subject to privacy regulations, a thorough AI security assessment also demonstrates due diligence in protecting that data from supply chain threats. As regulatory frameworks like the EU AI Act and emerging US state-level AI regulations begin to require supply chain documentation and proactive security measures, organizations that invest in supply chain visibility now will be better positioned for compliance.
The OWASP Top 10 for LLM Applications: Full Series
This article is part of our comprehensive series covering every vulnerability in the OWASP Top 10 for LLM Applications. Each article provides a deep dive into a specific risk category with practical defenses for your organization.
Prompt Injection
Published: February 25, 2026
Sensitive Information Disclosure
Published: February 26, 2026
Supply Chain Vulnerabilities
You are here
Data and Model Poisoning
Published: February 28, 2026
Insecure Output Handling
Coming soon
Excessive Agency
Coming soon
System Prompt Leakage
Coming soon
Vector and Embedding Weaknesses
Coming soon
Misinformation
Coming soon
Unbounded Consumption
Coming soon
Securing the Foundation Your AI Is Built On
Supply Chain Vulnerabilities sit at #3 in the OWASP Top 10 for LLM Applications because they threaten the very foundation of AI security. Unlike other vulnerabilities that exploit how an AI application is used, supply chain attacks compromise what the AI application is. When the model itself is backdoored, the training data is poisoned, or a critical library contains malicious code, every interaction with the AI system is potentially affected. The compromise is structural, not situational, and it can persist undetected for as long as the compromised component remains in production.
The AI supply chain is uniquely complex because it spans components that traditional security tools were never designed to evaluate. Model weights, serialized adapters, training datasets, and inference frameworks each present attack surfaces that require specialized knowledge to assess and defend. Organizations that apply only traditional software security practices to their AI deployments are leaving significant blind spots in their security posture, blind spots that sophisticated attackers are increasingly aware of and prepared to exploit.
For nonprofits, the path forward starts with visibility. You cannot defend what you cannot see, and most organizations lack a complete inventory of the AI components they depend on. Building that inventory, evaluating the provenance and integrity of each component, establishing trusted sourcing practices, implementing isolation and testing protocols, and maintaining continuous monitoring creates a layered defense that significantly reduces your supply chain risk. Not every organization will need to implement every layer at the same level of rigor, but every organization should understand their exposure and make deliberate decisions about which risks they accept and which they actively mitigate.
The most important step is to start. Map your AI components. Ask your vendors about their supply chain practices. Establish a testing pipeline for new components before they reach production. And if you are unsure about your current exposure, an AI security assessment can provide the comprehensive evaluation you need to understand your risk and take targeted action to reduce it.
Do You Know What Your AI Is Built From?
Supply Chain Vulnerabilities are the #3 risk in the OWASP Top 10 for LLM Applications, and traditional security tools cannot detect compromised models, poisoned datasets, or backdoored adapters. Our AI Application Security assessments map your complete AI supply chain, test component integrity, and identify the highest-risk dependencies in your deployment.
Start with a free consultation to understand your AI supply chain exposure and the right assessment scope for protecting your organization's AI deployments.