LLM Safety Assessment: The Definitive Guide on Avoiding Risk and Abuses

The generative artificial intelligence (AI) debate has engrossed the software industry and beyond ever since ChatGPT’s reveal in late 2022. For a year and a half, companies and individuals have rushed to share their thoughts on disruptive generative AI technologies, often glossing over specific merits and risks.

The lack of clarity around these emerging technologies has left many organizations concerned and overwhelmed, with some companies denying usage entirely. Others have permitted it to stay innovative, either allowing for restricted use or brushing off security concerns entirely. Regardless of the stance taken, generative AI isn’t going away, but it must be implemented and utilized safely. In order for this to happen, security teams must understand how these technologies can be abused.

With emerging technologies, companies often believe that they must keep their discoveries to themselves to gain an advantage against competitors. But obfuscating advancements does not maintain security, especially when the boundaries are being pushed daily. Developers must be willing to democratize the knowledge gained through the trial and error of emerging technologies, especially when this knowledge can impact the threat landscape.

While generative AI’s applications are growing by the day, the most prevalent example — the large language model (LLM) — has exploded in popularity for its ability to generate text-based insights, suggestions, conversions, and more. This report will discuss exactly how LLMs can be abused, explore the ten most common vulnerabilities, and highlight some of the mitigations available today to keep LLMs safe