Source URL: https://cloud.google.com/blog/products/identity-security/testing-your-llms-differently-security-updates-from-our-latest-cyber-snapshot-report/
Source: Cloud Blog
Title: Testing your LLMs differently: Security updates from our latest Cyber Snapshot Report
Feedly Summary: Web-based large-language models (LLM) are revolutionizing how we interact online. Instead of well-defined and structured queries, people can engage with applications and systems in a more natural and conversational manner — and the applications for this technology continue to expand.
While LLMs offer transformative business potential for organizations, their integration can also introduce new vulnerabilities, such as prompt injections and insecure output handling. Although web-based LLM applications can be assessed in much the same manner as traditional web applications, in our latest Cyber Snapshot Report we recommend that security teams update their approach to assessing and adapting existing security methodologies for LLMs.
Beware of prompt injections
An LLMs ability to accept non-structured prompts, in an attempt to “understand” what the user is asking, can expose security weaknesses and lead to exploitation. As general purpose LLMs rise in popularity, so do the number of user instances prompting the LLM to disclose sensitive information like usernames and passwords. Here is an example of this type of sensitive information disclosure from prompt injection:
A word on probability
Traditional web applications are typically deterministic. For any given input, the output of the application is reasonably guaranteed to be consistent (for example, 2 + 2 = 4). On the other hand, web-based LLMs are not deterministic but rather probabilistic. This is inherently due to an LLMs key objective taking shape as the attempt to mimic “understanding” of unstructured inputs.
Even with the same input, the LLMs responses will begin to differ: The user is not guaranteed the same output every time. Here’s an example:
Incorporating probabilistic testing can help provide better evaluation and protection against prompt injection, excessive agency, and overreliance. When it comes to prompt injections in particular, practitioners should identify what prompt and context were provided to the LLM when the vulnerability was discovered. Keeping this probabilistic nature in mind when assessing web-based LLMs will benefit the security professional on both offensive and defensive fronts.
Learn more
To learn more, read the latest issue of our Cyber Snapshot Report. In this report, we also dive into deploying deception strategies that combat adversaries targeting you, considerations for migrating from a legacy to a leading-edge SIEM platform, defending against the growing attacks on cloud-first organizations, and mitigating insider threats with the help of proactive penetration testing. You can read the full report here.
AI Summary and Description: Yes
Summary: The text discusses the transformative potential of web-based large language models (LLMs) while emphasizing the novel security vulnerabilities they introduce, particularly related to prompt injections and probabilistic outputs. It highlights the need for security teams to adapt their methodologies specifically for LLMs to ensure robust cybersecurity practices.
Detailed Description:
– **Introduction to LLMs**: Web-based large language models are changing online interactions by allowing users to engage in more natural and conversational methods rather than traditional query structures. This paradigm shift has significant implications for how organizations leverage technology.
– **Security Vulnerabilities**:
– **Prompt Injections**: LLMs’ ability to process non-structured prompts creates security weaknesses, exposing sensitive information such as usernames and passwords. This highlights the importance of recognizing how easily these models can be manipulated.
– **Probabilistic Outputs**: Unlike traditional deterministic applications, LLMs generate probabilistic outputs, leading to variability in responses even with the same input. This unpredictability presents challenges for establishing consistent security measures.
– **Assessment and Adaptation of Security Methodologies**:
– Security teams are recommended to evolve their assessment approaches to address the unique aspects of LLMs. This includes understanding that traditional web application security methodologies may not adequately cover LLM-specific vulnerabilities.
– **Probabilistic Testing**: The incorporation of probabilistic testing is suggested to better evaluate LLM behavior and provide enhancements against prompt injections and overreliance on model outputs.
– **Best Practices for Security Professionals**:
– Practitioners should focus on understanding and documenting the prompts and context of interactions that led to vulnerabilities.
– Emphasis on balancing offensive and defensive strategies in security assessments.
– **Broader Security Context**: The text connects the topic of LLM security to broader cybersecurity themes, promoting the significance of deception strategies, migration to advanced security information and event management (SIEM) platforms, and dealing with insider threats through proactive measures.
This information highlights the innovative challenges posed by LLMs for security professionals, emphasizing the integration of new strategies and approaches to mitigate risks effectively.