Introduction
Artificial intelligence is no longer a peripheral concern for security teams. As AI becomes embedded in production systems—from recommendation engines and fraud detection to code generation and autonomous agents—the attack surface it creates has grown substantially. AI security vulnerabilities are not hypothetical edge cases; they are active risks that affect real applications and real data, and they require the same rigor that security engineers apply to any other layer of the stack.
The urgency is compounded by how quickly the AI/ML ecosystem evolves. New frameworks, model architectures, and deployment patterns appear faster than most security programs can evaluate them. Teams running Java-based AI workloads, for example, may be pulling in dozens of Python-originated ML dependencies through interop layers, each carrying its own CVE history. The result is a rapidly expanding dependency graph that security engineers often have limited visibility into.
Learn more about AI security threats—from classic model-level attacks to the emerging risks of autonomous agentic AI systems—and practical guidance on how to avoid security threats posed by AI infrastructure.
What Are AI Security Vulnerabilities?
AI security vulnerabilities are weaknesses in AI and machine learning systems that can be exploited to compromise the confidentiality, integrity, or availability of a model or the data it processes. They overlap with traditional software security concerns—insecure dependencies, insufficient input validation, overly permissive access controls—but also introduce a category of risks unique to how ML systems learn and operate.
Unlike conventional software bugs, many AI security vulnerabilities are emergent properties of the learning process itself. A model trained on poisoned data will produce incorrect outputs even if the serving infrastructure is perfectly hardened. An adversarial input crafted to fool an image classifier does not look like a standard exploit—it may appear to a human observer as a perfectly normal image. This makes AI security a multi-domain problem: it requires expertise in both traditional software security and ML systems.
Three broad categories frame the risk landscape: model-level attacks (exploits that target the model itself, either during training or inference), infrastructure-level attacks (exploits that target the systems AI runs on, including dependencies, APIs, and supply chains), and operational risks (vulnerabilities that arise from how AI systems are deployed and used, including agentic behaviors, data handling, and access controls).
Common AI Security Threats
Prompt Injection
Prompt injection occurs when an attacker embeds malicious instructions inside input that an LLM processes, causing it to override its intended behavior. In practice, this can mean a user-supplied document containing hidden text that instructs a summarization model to exfiltrate data, or a web page that causes an AI browsing agent to take unauthorized actions. Prompt injection is particularly difficult to defend against because the same mechanism that makes LLMs flexible—their ability to follow instructions in natural language—is the vector of attack.
Model Poisoning
Model poisoning attacks compromise the training process by injecting malicious samples into the training dataset. A poisoned model may perform normally on standard inputs while producing attacker-controlled outputs for specific trigger conditions—a technique sometimes called a backdoor attack. In federated learning environments, where model updates are aggregated from multiple sources, poisoning can be introduced without access to the central training pipeline.
Data Leakage and Model Inversion
Language models and other ML systems can inadvertently memorize sensitive training data. Through carefully crafted queries, an attacker may be able to extract personal information, proprietary data, or credentials that appeared in the training corpus. Model inversion attacks take this further, reconstructing approximate representations of training samples from model outputs alone. For organizations training models on sensitive data—customer records, financial transactions, health information—this is a material privacy and compliance risk.
Adversarial Attacks
Adversarial inputs are crafted perturbations designed to cause a model to misclassify or behave unexpectedly. Originally studied in computer vision, adversarial techniques have been demonstrated across speech recognition, malware detection, and natural language processing. The practical threat is most acute in high-stakes applications: an adversarially crafted image that causes a medical imaging model to miss a diagnosis, or a payload that slips through an AI-based intrusion detection system.
Supply Chain Risks in AI/ML Pipelines
The AI/ML software supply chain is a significant and underappreciated attack surface. AI workloads typically depend on large ecosystems of open-source libraries—PyTorch, TensorFlow, Hugging Face Transformers, ONNX Runtime, and many others—each with their own dependency trees and CVE histories. Pre-trained models downloaded from public repositories may contain malicious code serialized into model weights. Compromised CI/CD pipelines can inject tampered artifacts into model training workflows. These supply chain risks are governed by the same principles as traditional software supply chain security, but the tooling to detect and respond to them in AI/ML contexts is still maturing.
Agentic AI Security Threats
Agentic AI—systems where AI models take sequences of autonomous actions, use tools, call APIs, browse the web, execute code, or interact with other agents—introduces a qualitatively different threat profile. Traditional AI security threats are largely passive: the model produces an output that a human or system then acts on. Agentic AI security threats arise because the model is itself an actor, capable of taking actions with real-world consequences.
The agentic AI security threat landscape includes several distinct risk categories: privilege escalation through tool use (an agent granted access to a set of tools may chain tool calls in ways that accumulate capabilities beyond what was intended), indirect prompt injection via the environment (agentic systems that process external content are exposed to injection at every step), cascading failures in multi-agent systems (a compromised sub-agent can propagate malicious instructions up the chain), unbounded resource consumption, and insufficient human oversight.
The agentic AI security threat landscape is evolving rapidly, and standards for securing these systems are still nascent. Teams deploying agentic AI in production should treat every external data source the agent consumes as untrusted input, and every action the agent can take as a potential attack surface.
How to Avoid Security Threats Posed by AI
Effective mitigation requires addressing AI security threats across multiple layers—model, infrastructure, application, and operational. No single control is sufficient.
Apply input validation and output filtering at every boundary. Treat model inputs as untrusted user data. Sanitize, validate, and scope inputs before they reach the model. Apply output filtering to detect and block responses that contain sensitive patterns, PII, or attempts to follow injected instructions.
Implement least-privilege access for AI systems. Scope the permissions granted to AI models and agents to the minimum necessary for their function. Apply the same zero-trust principles you would apply to a service account.
Audit and monitor the AI/ML dependency graph. Establish continuous monitoring of your AI/ML dependency tree—not just at build time, but in production. Know which versions of which libraries are running in each environment, and have a process for responding when a new vulnerability is disclosed.
Validate model provenance and artifact integrity. Verify checksums and signatures before using models in production. Treat model artifacts with the same supply chain controls you would apply to any software binary.
Establish logging, auditing, and rollback for agentic systems. Every tool call, API request, and data access should be logged with enough context to reconstruct what the agent did and why. Build in human-in-the-loop checkpoints for high-consequence actions.
Run adversarial testing and red-teaming. Integrate adversarial testing into the model development lifecycle. Red-team your models for prompt injection, jailbreaking, and data extraction before production deployment. Adversarial testing should be ongoing, not a one-time pre-launch exercise.
Conclusion
AI security vulnerabilities span a wide spectrum—from the statistical properties of machine learning models to the operational risks of autonomous agents that interact with production systems. As AI infrastructure matures and becomes more deeply embedded in enterprise software, the security implications become harder to ignore and harder to address with traditional tooling alone.
Java teams are not insulated from these risks. Whether you are running AI inference in a Java service, integrating with Python-based ML models over APIs, or adopting AI-powered tooling in your development pipeline, the AI/ML dependency graph intersects with your security posture in ways that deserve careful attention.
How Azul Can Help
If your team is running Java workloads that depend on AI/ML libraries—whether that means calling into TensorFlow Java, DL4J, ONNX Runtime, or integrating with LLM inference services over HTTP—the dependency security question is directly relevant to your AI security posture.
Azul Intelligence Cloud provides real-time visibility into the Java dependencies actually running in your production applications—without impacting performance. When a new CVE is published for a library your AI workload depends on, Intelligence Cloud’s Code Inventory feature lets you know whether the vulnerability is actually being used by your application, saving the time and resources spent on investigation. This is particularly valuable for AI/ML dependencies, which change frequently and carry a higher-than-average rate of security disclosures.
For teams running AI inference or other latency-sensitive Java workloads, Azul Prime adds predictable JVM performance on top of that security visibility. Zing’s C4 garbage collector eliminates stop-the-world pauses that can cause timeout failures in AI serving pipelines, andPrime’s production tooling helps engineering teams optimize and govern their Java AI infrastructure at scale.
