The Unique Challenges of Securing GenAI Systems
Generative AI (GenAI) has rapidly transformed the technology landscape, bringing unprecedented innovation across industries. However, as organizations increasingly integrate GenAI into their operations, they face unique and evolving security challenges. Common risks, such as prompt injections, adversarial attacks, and data leaks, are more sophisticated in the context of GenAI. Additionally, even minor updates or fine-tuning can significantly impact a model’s behavior, potentially introducing new vulnerabilities. Traditional methods of securing these systems, such as manual penetration testing, are no longer adequate.
A Real-World Use Case
Insurance Firm building GenAI application to support customer queries
Consider an insurance firm investing a couple of million dollars to build a GenAI chatbot for customer queries. The Director of AI at the firm is aware of the RoI of this project and also recognizes the significant security risks of deploying this chatbot at scale.
An attack objective might be to exploit vulnerabilities in the system to leak sensitive customer data, such as personal details or financial information. This type of breach could have devastating consequences, from regulatory penalties to a loss of customer trust. To address this, the team initiates a red teaming strategy to test the application at critical touchpoints, some of them being:
- Deploying a new model version after fine-tuning for improved accuracy.
- Updating the system prompt to refine application behavior.
- Modifying endpoint parameters, such as token size or language capabilities, based on new requirements.
- Adjusting the context window to enhance the customer experience.
- Incorporating new test cases released by security researchers to identify emerging attack techniques.
Upon further deliberation, the team realized that the anticipated frequency of updates to the application endpoint would be twice a month. The need for assessing security vulnerabilities at every step, therefore, should also follow a similar frequency. Moreover, insights from the assessments need to be delivered quickly to keep up with the pace of development.
Why Testing Against an Attack Prompt Once Isn't Enough?
In the realm of GenAI, testing an attack prompt a single time provides no guarantee of future security. Due to the model's probabilistic nature, an attack that fails initially may succeed on subsequent attempts.
What are the Challenges Posed by Evolving Threat Techniques?
Testing against one attack technique today versus in seven days can yield different results. The underlying model may have been updated, fine-tuned, or may now be more susceptible to new attack methods that have emerged.
Why Manual Red Teaming Isn’t Enough for GenAI
If an enterprise decides to depend on a manual workforce to run these tests, the scale quickly becomes unmanageable:
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Volume of Tests
With several use cases and numerous potential attack prompts for each, the number of tests required balloons into the thousands. Each attack prompt needs to be tested multiple times to account for the non-deterministic nature of the model.
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Resource Intensive
Assigning human testers to perform these tasks would require a substantial workforce. Each tester would need to meticulously craft prompts, execute them repeatedly, and document the outcomes. This process is not only time-consuming but also prone to human error.
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Frequency of Testing
Given that model behaviors and threat landscapes change rapidly, these tests would need to be conducted frequently—ideally once every sprint cycle. Keeping up with this pace manually is virtually impossible.
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Delayed Response to Emerging Threats
Manual teams cannot adapt quickly enough to new vulnerabilities that appear in real-time. This lag leaves the system exposed during the interim, increasing the risk of a successful attack.
Automated Red Teaming for GenAI is the Solution
Automating a red teaming exercise addresses these challenges effectively:
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Scalability
An automated testing process can run thousands of tests across all 300 use cases simultaneously, ensuring comprehensive coverage without needing a large manual team.
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Continuous Testing
An automated red teaming toolkit can operate 24/7, repeatedly testing attack prompts to account for the model's variability and adapting to any changes in the model's behavior, on-demand or as triggered.
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Rapid Integration of New Attack Techniques
It is easier to regularly update an automated solution with the latest attack vectors and techniques, as compared to training a large red teaming workforce.
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Efficient Resource Utilization
By automating the testing process, the enterprise can allocate critical resources to focus on mitigating identified vulnerabilities rather than searching for them.
When Should You Red Team Your GenAI Application
Given the challenges of manual testing, understanding when and how to conduct automated red teaming scans for GenAI applications is crucial:
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Whenever the Attack Library Gets Updated
New vulnerabilities are constantly being identified, making it essential to run red teaming exercises whenever your attack library is updated. Automated solutions like Protect AI’s Recon make it easy to conduct these tests as soon as new attack vectors are added.
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After Each Change to the Application Endpoint
Any modification to the application’s endpoint during the development and build phases can introduce new vulnerabilities. Automated checks should be conducted after significant updates to ensure these changes have not created additional risks.
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Regularly Throughout the Development Lifecycle
For organizations with continuous integration/continuous deployment (CI/CD) pipelines, frequent updates are a norm. Automated security tests should be embedded as a recurring part of the development process to maintain consistent security assurance.
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When Expanding Model Capabilities
Adding new functionalities or adapting your model for different use cases can expose it to new vulnerabilities. Automated red teaming ensures that these transitions are secure, highlighting potential issues before deployment.
What does an effective Automated Red Teaming Solution look like?
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Comprehensive Threat Modeling
Automated red teaming systems should model threats across all threat vectors known to the GenAI ecosystem. They should be usable across the entire AI lifecycle including model training, deployment, and testing simulated runtime interactions.
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Automated Adversarial Testing
The best solutions should use AI-powered tools for adversarial prompt generation, bypass guardrails, and exploit vulnerabilities like data poisoning or instruction tuning.
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Domain Specific Red Teaming
An ideal solution should be able to simulate real-world attack scenarios dynamically, incorporating contextual, cultural, and ethical factors relevant to specific regions or business domains.
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High Usability
These solutions should be easily integrated into the existing environments that AI teams are using. Any time saved on conducting a red teaming exercise can be leveraged in building defenses against the evolving threat vectors.
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Multi-Modal Testing Capability
Ensure the system can evaluate vulnerabilities across text, images, audio, or multimodal inputs to align with diverse AI use cases.
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Automated Report Generation
Provide detailed, actionable reports categorizing vulnerabilities by severity, impact, and recommended remediation strategies.
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Governance and Compliance
The insights generated from the automated processes should align with regulatory standards like NIST AI RMF, the MITRE ATLAS framework, the OWASP guidelines, and the EU AI Safety Act.
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Feedback Loop for Model Improvements
Delivering actionable insights based on the red teaming exercise is a must have to improve GenAI security. At the same time, if the solution allows for a feedback loop to improve red teaming activity, it’s a win-win for the GenAI ecosystem because this builds a robust flywheel and fosters collaboration
Why Protect AI’s - AI Red Teaming Solution Stands Out
ProtectAI’s AI Red Teaming tool, Recon, offers a unique approach that combines the use of an extensive attack library with an LLM agent red teaming your GenAI system. Recon’s capabilities can be best explained by using the example of the AI charter at the insurance firm discussed above.
Example: A Insurance Firm using Protect AI's Recon
The Director of AI at the insurance firm leading the AI safety charter can use ProtectAI’s Recon and reap the following benefits:
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Zero effort Endpoint configuration
Recon is built for security engineers to configure their application endpoints in less than 5 minutes. Recon also allows you to configure custom triggers to initiate scans based on version updates.
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Comprehensive threat coverage
Recon scans for 6 out of the 10 OWASP top 10 vulnerabilities for LLMs, with several more in the roadmap. Its proprietary attack library alone scans for more than 50 different techniques.
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Agentic Domain Specific Red Teaming
LLMs can be creative. Recon conducts red teaming of your GenAI system, not just with a library of attacks but also using a trained language model that understands the context of the application and delivers insights on existing vulnerabilities.
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Save Cost on selecting the more secure model
Selecting a more secure model that suits your business use case can be a costly iterative process. Automated red teaming of these foundation models using Recon significantly reduces that cost.
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Reduce time to market for the GenAI application
Combined with the benefits mentioned above, Recon delivers crisp actionable insights and remediation recommendations for the vulnerabilities identified during scans. You can make informed decisions, build better prioritization frameworks, and essentially, ship faster.
In addition to this, Protect AI offers flexible deployment options that reduce the cost of installing, running, and maintaining a red teaming solution that uses an AI agent, while complying with the organization’s security requirements.
Automated Red Teaming is the Future of AI Security
The landscape of AI security is evolving alongside the technology itself, with new regulations and compliance standards highlighting the importance of robust security measures. Automated red teaming is becoming a strategic necessity, not just a best practice. Tools like Protect AI's Recon help fortify their security posture against the complex threats posed by Generative AI.
To learn more about how Protect AI’s AI Red Teaming capabilities can revolutionize your GenAI security strategy, book a demo today.
