There’s a Critical Need for Robust Security Testing Solutions as Businesses Harness the Power of AI

News

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3. Are Organizations Investing in AI Achieving Positive ROI?

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5. What Are the Challenges Firms in the UAE and Saudi Arabia Face in GenAI Implementation?

6. Enterprises See the Promise of Generative AI But Lack the Guardrails to Mitigate Operational Risks

Organizations are increasingly reliant on GenAI models and Language Learning Models (LLMs) like ChatGPT. According to reports, two-thirds of organizations have adopted or are exploring AI for a more efficient and automated workflow. However, with the rise in AI use, there’s a critical need for robust security testing solutions.

Last year, there was an outage with OpenAI’s AI tool, ChatGPT. The outage was caused by a vulnerability in an open-source library, which may have exposed some customers’ payment-related information. In January, a critical vulnerability was discovered in Anything LLM that turns any document or content into a context that any LLM can use during chatting. 

An unauthenticated API route (file export) can allow attackers to crash the server, resulting in a denial of service attack. These are only a few examples of security incidents related to using LLM models.

On average, an organization uses ten GenAI models. Often, most LLMs in production will receive data indirectly via APIs. That means tons and tons of sensitive data is being processed by the LLM APIs. 

There are several ways in which LLMs can be abused, resulting in data breaches.

Prompt Injection Vulnerabilities: The risk of unauthorized prompt injections, where malicious inputs can manipulate the LLM’s output, has become a major concern.

Denial of Service (DoS) Threats: LLMs are also susceptible to DoS attacks, where the system is overloaded with requests, leading to service disruptions. There’s been a rise in reported DoS incidents targeting LLM APIs in the last year.

Overreliance on LLM Outputs: Overreliance on LLMs without adequate verification mechanisms has led to data inaccuracies and leaks. Organizations are encouraged to implement robust validation processes as the industry sees increased data leak incidents due to overreliance on LLMs.

Ensuring the security of APIs to protect user privacy and prevent data leaks is crucial. It means securing GenAI systems not only the AI from external inputs but also external systems that depend on their outputs.

Akto has launched a proactive security testing platform, GenAI Security Testing solution, that can scan APIs that leverage AI technology. This is considered fundamental for the future of application security. 

Akto’s security testing solution leverages advanced testing methodologies and algorithms to provide comprehensive security assessments for GenAI models, including LLMs. 

The solution incorporates many innovative features, including over 60 meticulously designed test cases covering various GenAI vulnerabilities, such as prompt injection, overreliance on specific data sources, and more. 

Security teams manually test all the LLM APIs for flaws before release. Due to the time-sensitivity of product releases, teams can only test for a few vulnerabilities. As hackers continue to find more creative ways to exploit LLMs, security teams must find an automated way to secure LLMs at scale.

AI security testing identifies vulnerabilities in the security measures for sanitizing the output of LLMs. It aims to detect attempts to inject malicious code for remote execution, cross-site scripting, and other attacks that could allow attackers to extract session tokens and system information. In addition, Akto also tests whether the LLMs are susceptible to generating false or irrelevant reports.

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