So you're building an AI feature powered by a Large Language Model. Who isn't nowadays? It's quick to build a prototype. You can integrate with the OpenAI API or Anthropic API within minutes. Play around with model inputs. Prompt optimization is a bit painful, but getting a satisfying response from the language model is actually incredibly fast.

Before you get too excited, let's take a step back. While LLMs are advancing the tech landscape, they're not without their pitfalls. Overlooking the risks can lead to unhappy users, security breaches, or even legal issues. Let's explore the security, engineering, and safety risks you should keep on your radar, addressing both technical and ethical challenges.

Security Risks

There are many security risks while deploying LLM features. These risks range from the model performing inappropriately to users deliberately misusing its capabilities. There is no better authority on this topic than the Open Web Application Security Project, which released a list of the 10 most critical vulnerabilities often seen in LLM applications (check out the OWASP LLM vulnerabilities here):

1. Prompt Injection

Manipulating LLMs via crafted inputs can lead to unauthorized access, data breaches, and compromised decision-making.

2. Insecure Output Handling

Neglecting to validate LLM outputs may lead to downstream security exploits, including code execution that compromises systems and exposes data.

3. Training Data Poisoning

Tampered training data can impair LLM models, leading to responses that may compromise security, accuracy, or ethical behavior.

4. Model Denial of Service

Overloading LLMs with resource-heavy operations can cause service disruptions and increased costs.

5. Supply Chain Vulnerabilities

Depending upon compromised components, services, or datasets undermines system integrity, causing data breaches and system failures.

6. Sensitive Information Disclosure

Failure to protect against disclosure of sensitive information in LLM outputs can result in legal consequences or a loss of competitive advantage.

7. Insecure Plugin Design

LLM plugins processing untrusted inputs and having insufficient access controls risk severe exploits like remote code execution.

8. Excessive Agency

Granting LLMs unchecked autonomy to take action can lead to unintended consequences, jeopardizing reliability, privacy, and trust.

9. Overreliance

Failing to critically assess LLM outputs can lead to compromised decision-making, security vulnerabilities, and legal liabilities.

10. Model Theft

Unauthorized access to proprietary large language models risks theft, competitive advantage, and dissemination of sensitive information.

We’d like to add these additional security risks to that list:

Data Privacy

When user data is sent to third-party APIs, there's a risk of exposing sensitive information, especially if data protection measures aren't properly implemented.

Example: Personal identifiers or confidential business information could be inadvertently logged or leaked.

Mitigation: Anonymize data before processing, use encryption, implement strict access controls, and ensure compliance with regulations like GDPR or HIPAA. Establish robust incident response plans to handle any data breaches promptly and effectively.

We've written an article on how to ensure you're good on the legal side:
Responsible AI Deployment with Requesty's Terms of User Monitoring

Dependency on External Services

Relying on third-party APIs means you're at the mercy of their uptime and policies.

Risk: Service outages, security threats, or changes in usage terms can disrupt your application and affect user experience.

Mitigation: Implement fallback mechanisms, monitor service level agreements (SLAs) of your providers, and stay vigilant about potential security threats from these external services.

Engineering Risks

Scalability and Performance

LLM systems are resource-intensive. As your user base grows, so do the demands on your infrastructure, which can lead to latency issues, security risks, and increased costs.

Example: A sudden spike in users could overwhelm your system, causing slow responses or timeouts.

Mitigation: Optimize your application's performance, use caching strategies, and consider load balancing to handle high traffic.

Non-Deterministic Outputs

LLMs can produce different responses to the same input, making it hard to predict behavior and ensure consistency.

Example: Inconsistent answers from a database-facing AI feature can return the wrong responses, or even break the product experience.

Mitigation: Set the model's temperature parameter appropriately and consider using deterministic modes when consistency is crucial.

Testing and Debugging Challenges

Traditional testing methodologies don't always apply to AI models, complicating both the analysis and addressing of issues.

Example: Unpredictable AI behavior might will slip through unit tests designed for deterministic code.

Mitigation: Develop AI-specific testing frameworks (evals, evals, evals) and include human-in-the-loop evaluations to catch anomalies.

Monitoring and Maintenance

AI models and APIs are constantly evolving and advancing. Updates can change how your application behaves, sometimes breaking existing features.

Example: An API update might improve language understanding but introduce new bugs or incompatibilities.

Mitigation: Keep up to date of updates from your AI providers, and thoroughly test your application whenever underlying model configurations are changed. During usage, make sure to set up alerts and sample testing of real user interactions.

Cost Overruns

API usage costs can escalate quickly, especially if your application makes frequent or complex requests.

Example: Without proper monitoring, you might receive an unexpected bill that far exceeds your budget.

Mitigation: Implement usage tracking, set up budget alerts, and optimize prompts to reduce token consumption.

Safety Risks

While security and engineering challenges are significant, safety risks also deserve attention. LLMs learn from both the good and the bad of humankind, which means they can inadvertently produce outputs that are biased, offensive, or harmful.

We wrote an article about the Safety Hazards of AI which goes into much more detail:
Safely Deploy Public Facing AI - Detecting Breaches with Request MLCommons Aligned Solutions

Mitigation: Implement content filtering and moderation strategies. Regularly audit your model's outputs for inappropriate content. Fine-tune the model with curated datasets to reduce biases and ensure compliance with ethical standards. Ensuring accurate and unbiased information is crucial for maintaining user trust and safety.

"How can I deploy this LLM feature safely?"

Building AI features with LLMs is extremely exciting and opens up a world of possibilities. However, it's essential to navigate the associated security, engineering, and safety risks thoughtfully. By proactively addressing these challenges such as data protection, accurate information handling, and implementing robust access controls, you can create powerful, responsible AI applications that serve your users well and stand the test of time. Hopefully, you now have plenty of food for thought.

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Common Questions About LLM Risks

What are the dangers of LLM?

LLMs can produce inaccurate or misleading information, leading to poor decision-making. They may inadvertently generate biased or inappropriate content, pose security risks through vulnerabilities like prompt injection, and expose sensitive data if not properly managed.

What is LLM vulnerability?

LLM vulnerability refers to weaknesses in large language models that can be exploited to compromise security, privacy, or functionality. Examples include prompt injection attacks, data poisoning, and overreliance on model outputs without proper validation.

What are some of the challenges of using LLMs?

Challenges include ensuring data privacy, managing computational resources, handling non-deterministic outputs, mitigating biases, and integrating the models into existing systems. Additionally, the lack of transparency in how LLMs make decisions can complicate debugging and compliance efforts.

What are the criticisms of LLM?

Criticisms of LLMs revolve around their tendency to generate plausible but incorrect or biased information, high computational costs, environmental impact due to energy consumption, and ethical concerns over replicating and amplifying societal biases present in training data.

Reach out if you need support with any of these challenges: Contact Us