Let’s consider launching a sleek new AI assistant. This will help you serve your customers more effectively. A user visits your website and makes a few innocent requests for the assistant. It cheerfully provides responses that include confidential data. Now, think that the user is not the customer. He is an attacker who skillfully manipulates your AI to reveal your confidential information.
This is not imaginary. As the pace of launching services and apps based on large language models (LLMs) and generative AI (GenAI) accelerates, some companies are becoming easy targets for this type of attack. Cybercriminals are actively exploiting and exposing LLMs’ vulnerabilities to gain access to your critical data and systems. This highlights the importance of LLM security testing.
Introduction
Deceiving AI Into Breaking The Rules
For example, previous year, threat actors found a vulnerability in ChatGPT-Next-Web that permitted them to bypass access controls and gain more access to internal mechanisms. After entering, the attacker could enter a company’s infrastructure. This attack plan can be automated and scaled to target numerous ecosystems. This is going to put unpatched mechanisms at risk.
This example pinpoints a main vulnerability to the AI mechanism. They need to incorporate numerous APIs to access services and data to complete their duties. These APIs are a good target for the attackers.
The Unexpected Risk
Combining the risks is the quick adoption of LLMs and GenAI by divisions across the company. This is creating an expansion of APIs outside traditional IT procedures. This develops a proliferating issue of shadow APIs that have fewer safeguards, documentation, and proper visibility.
AI systems can now produce their APIs vigorously. This is without any human involvement. That takes the shadow API threats to a completely new threat level. It develops a possibility of vulnerabilities you are not informed of.
How LLM Adoption Is Reshaping The Risk Landscape?
Here are four ways LLM adoption is reshaping the risk landscape.
- Behavior and Purpose: Legacy APIs are deterministic, generating foreseen outcomes. LLM APIs and AI are non-deterministic. They can produce various results, creating defense and testing very complex.
- Data Usage: Legacy APIs are sometimes associated with particular integrations, business logic, and transactions. LLMs APIs and AI can perhaps use and produce confidential data. This develops possible security and privacy risks.
- Different Security Risks: Legacy APIs are vulnerable to gaps in well-understood problems, authentication, and configuration. LLM APIs and AI show the latest attack avenues. This entails hallucination, jailbreaks, model abuse, and prompt injections.
- Governance: Legacy APU security concentrates on well-established plans, like authentication, data validation, and rate limiting. LLM APIs and AI need extra monitoring and safeguards, entailing response management, granular policy enforcement to govern prompts, content classification, and more traffic inspection.
How Can You Update Your API Security Strategy?
Here are some ways you can update your API security strategy:
- Complete API discovery, tracing, and testing so you know what is out there in your ecosystem at all times, entailing documenting and pinpointing dynamically produced API calls and APIs.
- Applying security plans that manage immediate evaluation of the latest APIs, not only those already registered.
- Guaranteeing that all APIs entail proper restrictions to protect against data leakages.
- Applying micro-segmentation to safeguard lateral movement within your ecosystem, an attacker attains access through the LLM API and AI.
Creating The Business Case
Positioning LLM and Gen AI apps provides the latest possibilities for attaining a competitive advantage. However, it also shows security complexities that have budgetary implications. How can you apply the security required to safeguard against threats aimed at LLMs without neglecting the economic advantages in your AI plan?
Security leaders need to bring these risks to the business early, prior to any incident that does it for them. This is important to attain the sponsorship and support required to fund the security enhancements needed to safely apply your LLM and AI plan.
It is worth considering that the misgivings about investing in security sometimes disappear rapidly after a company has experienced a breach. Showing the upside of ignoring the breach- and the reputational and financial costs-must be part of any business LLM and GenAI adoption plan.
Guaranteeing that your security plan is ready for LLM. This is the first step to make your company ready for LLM. However, in today’s AI period, it just takes one missed API to make headlines for tomorrow.
Frequently Asked Questions (FAQs)
What is meant by LLM security testing?
It is a systematic assessment of AI language frameworks to pinpoint possible risks and vulnerabilities. Specifically, these mechanisms are being positioned across web apps at an important rate.
What is the full form of LLM in testing?
It stands for Large Language Model.
What are the 5 tips to use LLM for security?
- Incorporate federated learning for distributed and safe training
- Position a red team approach to assess LLM vulnerabilities
- Apply output risk scoring for sensitive answers
- Utilize fine-grained prompt control
- Incorporate synthetic observation for regular model activity.
