Can Common Cyber Security Tools Handle Large Language Model Risks? Part 2.

Companies are increasingly turning to Large Language Models (LLMs) to streamline and enhance various aspects of their core activities. As LLMs become more deeply integrated into organizational workflows, their significance in everyday business operations is growing. However, this rise is accompanied by an increase in the risks associated with LLMs, making them attractive targets for threat actors.

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Traditional cybersecurity solutions have proven effective in the specific areas and challenges they were designed to address. But they may fall short when it comes to securing LLMs against new threats - both known and as yet unknown.  In our previous article, we discussed how Browser Security, DLP, and DSPM frameworks and their limitations when it comes to safeguarding LLMs.

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In this article we will explore the shortcomings of SaaS Security Posture Management (SSPM), API Security and Cloud Security Posture Management (CSPM).

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SaaS Security Tools and LLM Security

SaasS Security Posture Management (SSPM) refers to the practice of continuously monitoring and enhancing the security of Software-as-a-Service (SaaS) applications. These tools provide visibility into the security posture of an organization's SaaS ecosystem, enforcing policies, and offering insights and recommendations to enhance security measures and reduce the risk of data breaches and other cyber threats.

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Core components include discovering and managing SaaS applications, ensuring compliance, and assessing risks. SSPM tools can also automate remediation measures to protect against data breaches and cyber threats.

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The problem is that SSPMs can only cover a small part of an LLM application’s attack surface. They may have some ability to detect and even respond to breaches, this is too limited to provide real security for LLMs. In addition, they have no ability to address shadow LLM concerns and detect the type and extent LLM usage within an organization.

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Gaps Between API and LLM Security

Many LLMs are accessed through APIs. CSPM tools focus on securing infrastructure APIs, but do not necessarily protect the APIs that interact with LLMs. Proper API security, including authentication, authorization, and rate limiting, are all crucial.

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API security centers on protecting the Application Programming Interfaces (APIs) that enable communication and data exchange between different software systems. Therefore their strength is at safeguarding unauthorized access and tokens and API infrastructure. 

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LLM security security, on the other hand, specifically focuses on the security of systems employing generative artificial intelligence models, such as GPT (Generative Pre-trained Transformer) and as such monitor and secure also direct Web-Based interaction such as ChatGPT, as well as other 3rd party tools, which API security is lacking. 

Fail in Dynamic Language Response 

The most important gap as we compare both solutions lies in the fundamental LLM capabilities based on Language response. While API Security shields from a structural response, The response received by LLM-based tools is dynamic and could be easily bypassed leaving your organization vulnerable. 

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The Limitations of CSPM in Securing Large Language Models

Cloud Security Posture Management (CSPM) is an invaluable tool, or concept for safeguarding cloud infrastructure. CSPM tools are primarily designed to assess the configuration and security posture of cloud resources and infrastructure. They excel at identifying misconfigurations, vulnerabilities, and compliance violations within the infrastructure.

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Of course, CSPM is irrelevant for LLMs that run on-premise. But even for cloud-based applications, LLMs pose unique challenges that extend beyond the scope of CSPM:

Limited Focus on Access Control

CSPM primarily focuses on the configuration of cloud resources. While it ensures that cloud infrastructure is correctly configured, it doesn't address finer-grained access control for LLMs. Securing LLMs involves preventing the leakage of crucial internal data by controlling who can interact with them, not just how they are hosted.

User Behavior and Inputs

LLMs exhibit dynamic behavior, responding to a wide range of inputs. CSPM is ill-equipped to analyze the nature of inputs and the appropriateness of responses. This leaves plenty of room for misuse or exploitation.

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Finally, while CSPM can monitor infrastructure for misconfigurations, it may not provide the depth of monitoring needed to detect unusual or potentially malicious interactions with LLMs. Specialized monitoring solutions are required to capture user behavior and model outputs.

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Embracing Comprehensive Security for LLMs

While SSPM, API security, and CSPM offer foundational security measures, they are not sufficient to cover the full spectrum of challenges presented by Large Language Models. Our LLM-focused cybersecurity solutions bridge this gap, ensuring robust and comprehensive protection. Get in touch to find out how our approach empowers organizations to navigate LLM security challenges with confidence and harness the full potential of their LLM investments securely.