# Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential

arXiv: https://arxiv.org/abs/2509.10655 · HTML: https://mlsanity.com/paper/2509.10655/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling](https://mlsanity.com/paper/2601.22636/) (2026)
- [Adversarial Confusion Attack: Disrupting Multimodal Large Language Models](https://mlsanity.com/paper/2511.20494/) (2025)
- [Left, Right, or Center? Evaluating LLM Framing in News Classification and Generation](https://mlsanity.com/paper/2601.05835/) (2026)

## Abstract (arXiv)

While the widespread deployment of Large Language Models (LLMs) holds great potential for society, their vulnerabilities to adversarial manipulation and exploitation can pose serious safety, security, and ethical risks. As new threats continue to emerge, it becomes critically necessary to assess the landscape of LLMs' safety and security against evolving adversarial prompt techniques. To understand the behavior of LLMs, this research provides an empirical analysis and risk profile of nine prominent LLMs, Claude Opus 4, DeepSeek V3 (both open-source and online), Gemini 2.5 Flash, GPT-4o, Grok 3, Llama 4 Scout, Mistral 7B, and Qwen 3 1.7B, against 24 different security and safety categories. These LLMs are evaluated on their ability to produce harmful responses for adversarially crafted prompts (dataset has been made public) for a broad range of safety and security topics, such as promotion of violent criminal behavior, promotion of non-violent criminal activity, societal harms related to safety, illegal sexual content, dangerous code generation, and cybersecurity threats beyond code. Our study introduces the Risk Severity Index (RSI), an agile and scalable evaluation score, to quantify and compare the security posture and creating a risk profile of LLMs. As the LLM development landscape progresses, the RSI is intended to be a valuable metric for comparing the risks of LLMs across evolving threats. This research finds widespread vulnerabilities in the safety filters of the LLMs tested and highlights the urgent need for stronger alignment, responsible deployment practices, and model governance, particularly for open-access and rapidly iterated models.