The Truth About AI Security

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Today I speak to you about something that will determine the future of our digital world. About Large Language Models. About artificial intelligence. And about the security risks that nobody wants to see, but everyone must know.

The OWASP Foundation has published the Top Ten security risks for Large Language Model applications Twenty Twenty Five. This is not just a list. This is a wake-up call. A manifesto for everyone who wants to understand what's at stake.

## Risk Number One: Prompt Injection - The Achilles Heel of AI

Imagine you've built the perfect system. A Large Language Model that delights your customers, revolutionizes your processes, transforms your business. And then someone comes along with a few clever words and brings it all crashing down.

That's Prompt Injection. The manipulation of inputs to force the Large Language Model into unwanted actions. From bypassing security controls to disclosing sensitive data. A nightmare scenario for any company.

But here's the truth: It's preventable. Strict input validation, authorization controls, separation of system and user inputs. It's a constant race between attackers and developers. But with the right measures, we win.

## Risk Number Two: Sensitive Information Disclosure - When AI Becomes a Chatterbox

What happens when your Large Language Model spills sensitive data? When it inadvertently reveals business secrets, personal data, or access credentials? That's called Sensitive Information Disclosure. And it can be devastating.

Imagine this: A Large Language Model for customer service accidentally reveals another customer's data. An internal Large Language Model betrays details about proprietary algorithms. The consequences range from reputational damage to hefty fines.

The solution? Robust data cleaning and anonymization. Strict access controls on sensitive data. Filtering Large Language Model outputs. Only this way do we ensure our Large Language Models don't become a leak.

## Risk Number Three: Supply Chain - The Invisible Threat

We secure our applications. But how secure is the supply chain of our Large Language Models? Supply chain security is an often overlooked but critical aspect. From manipulated training data to compromised open-source models to insecure libraries.

An attacker could embed a backdoor in a pre-trained model. Slip in a manipulated library that then gets used in countless applications. The consequences would be catastrophic: from executing malicious code to widespread system failures.

Secure development practices throughout the entire lifecycle. Verification of all components from trusted sources. Regular security audits. We must protect our supply chains as rigorously as our own applications.

## Risk Number Four: Data and Model Poisoning - Poisoned Data, Poisoned Results

What happens when the data used to train a Large Language Model is manipulated? Data and Model Poisoning is an insidious attack method. Vulnerabilities, backdoors, or biases are injected into a Large Language Model. The result: The model behaves unpredictably and potentially harmfully.

An attacker could manipulate the training dataset so the model generates racist or discriminatory statements. Or build in sleeper agents that are activated by certain triggers and compromise the system.

Protection requires a robust process for validating and cleaning training data. Strict access controls. Exclusive use of trusted data sources. The origin of data must be carefully tracked.

## Risk Number Five: Improper Output Handling - When Output Becomes a Weapon

A Large Language Model generates a response – and that response brings down your entire application. Improper Output Handling is a critical security risk. Large Language Model outputs are not sufficiently validated, cleaned, or handled before being passed to downstream components.

Imagine this: A Large Language Model generates malicious JavaScript code that executes in a web application. An SQL injection payload that compromises your database. The consequences range from Cross-Site Scripting and Denial of Service to complete system compromise.

All Large Language Model outputs must be validated and cleaned. Strict access controls. Filtering outputs based on context. Encoding outputs to prevent malicious code execution. Never trust Large Language Model outputs blindly.

## Risk Number Six: Excessive Agency - When the Large Language Model Takes Control

A Large Language Model based application with too much autonomy and too little control can lead to unexpected and harmful actions. This is called Excessive Agency. Triggered by hallucinations, prompt injection, or compromised extensions.

Imagine an AI agent with uncontrolled access to internal systems performing critical financial transactions without human approval. An agent interacting with an external tool, accessing sensitive data and manipulating it.

Strict access controls for Large Language Model agents. Limiting autonomy to the necessary minimum. Implementing human-in-the-loop mechanisms. Kill-switch mechanisms as the last line of defense.

## Risk Number Seven: System Prompt Leakage - The Best-Kept Secret

System prompts are the secret instructions that control a Large Language Model's behavior. But what happens when this sensitive information becomes public? System Prompt Leakage is a serious security risk.

These prompts should never be considered security controls, as they may contain sensitive data like access credentials. An attacker who knows the system prompt can manipulate the Large Language Model, bypass security controls, and exfiltrate private information.

Never store sensitive data in system prompts. Strict separation of harmful content through external systems. Implement critical controls outside the Large Language Model.

## Risk Number Eight: Vector and Embedding Weaknesses - The Achilles Heel of Retrieval Augmented Generation

Retrieval Augmented Generation is a powerful technique that enhances Large Language Model responses through external knowledge sources. But the underlying vector and embedding mechanisms carry their own risks.

Errors in vectorization models, lack of input validation, or insufficient protection of embedding databases can lead to manipulated embeddings. The Large Language Model misidentifies objects, reveals sensitive information, or delivers falsified search results.

Inputs for vector generation must be validated and cleaned. Strict access controls on embedding databases. Secure storage and transmission of embeddings. Use of robust vectorization models.

## Risk Number Nine: Misinformation - Truth or Fiction?

Large Language Models can sound convincing, but what if they spread false or misleading information? Misinformation, also known as hallucinations, is a widespread problem. Large Language Models can generate fake news articles, discriminatory content, or even false medical diagnoses.

The causes are varied: insufficient fact-checking, use of unreliable data sources, prompt injection, or systematic biases in training data.

Large Language Model outputs must be actively fact-checked. Use exclusively high-quality and trusted data sources. Review outputs by context. Manual verification of critical outputs. Clear communication of information sources.

## Risk Number Ten: Unbounded Consumption - When the Large Language Model Becomes a Bottomless Pit

Uncontrolled Large Language Model applications can lead to excessive and uncontrolled resource consumption. This is called Unbounded Consumption. This can lead to Denial of Service, financial losses, model theft, and degradation of service quality.

Imagine Large Language Model agents in an endless loop accessing external APIs. Large Language Models flooded with complex queries. The consequences are severe: from impaired availability and performance to significant financial costs from uncontrolled resource consumption.

Rate limiting and quotas are essential. Resource consumption must be permanently monitored. Inputs validated to prevent Denial of Service attacks. Systems designed to be scalable and regularly tested for vulnerabilities.

## The Large Language Model and Generative AI Application Security Operations Framework

Security for Large Language Models and generative AI applications is more than just an afterthought. The OWASP Large Language Model and Generative AI Application Security Operations Framework provides a comprehensive approach to integrate security into every step of the development cycle – from planning to operations.

The framework is divided into various phases: Plan and Scope for defining security requirements and risk assessment. Development and Experiment for secure development and experiments with Large Language Models. Test and Evaluation for comprehensive testing for vulnerabilities and performance. Release, Deploy, Operate for secure deployment, operation, and monitoring.

## The Artificial Intelligence Controls Matrix

How can we ensure AI systems are safe and trustworthy? The Artificial Intelligence Controls Matrix from the Cloud Security Alliance provides a comprehensive framework that helps companies assess and manage AI system risks.

The matrix includes Core Controls as fundamental controls for AI systems. AI Consensus Assessment Initiative Questionnaire as a questionnaire for assessing AI security. Implementation Guidelines as instructions for implementing controls. Auditing Guidelines as guidelines for auditing AI systems.

Additionally, the matrix provides mappings to important standards and regulations like ISO forty two thousand and one, the European Union AI Act, and NIST six hundred dash one.

## The Future Belongs to Those Who Shape It Securely

We stand at a turning point. Large Language Models and generative AI will change our world. But only if we make them secure. Only if we understand and master the risks.

The OWASP Top Ten for Large Language Model Applications Twenty Twenty Five is not just a warning. It's a call to action. A guide for everyone who wants to harness AI's full potential without sacrificing security.

The technology is here. The frameworks are available. The controls are defined.

The only question is: Are you ready to implement them?

The future doesn't belong to those who build the most powerful AI systems. It belongs to those who build the safest ones. To those who understand that true innovation isn't about crossing boundaries, but setting them intelligently.

Security by Design isn't optional. It's the ticket to the future of artificial intelligence.