LLM Pen Testing Tools in Cybersecurity

In the ever-advancing landscape of cybersecurity, innovation serves as the vanguard against emerging threats. Large Language Models (LLMs) stand as the latest frontier in this ongoing battle, presenting a complex tapestry of opportunities and vulnerabilities. Prompt injection and jailbreaking techniques have emerged as indispensable methodologies in probing the resilience of these models, expanding the horizons of their capabilities while uncovering potential weaknesses.

Amidst the recent limelight cast upon Microsoft's PyRIT, a constellation of LLM pen testing tools beckons attention, each a beacon of ingenuity crafted by researchers and cybersecurity aficionados. Delving into this sphere reveals a tapestry of innovation and collaborative endeavor:

1. garak by Leon Derczynski

Derczynski's garak stands as a testament to the potency of prompt injection techniques, providing a lens through which to scrutinize the fortitude of LLMs in the face of adversarial inputs.

2. HouYi by YI LIU and Gelei Deng

HouYi's contribution lies in its innovative approach to LLM security, illuminating potential vulnerabilities and avenues of exploitation within these models.

3. JailbreakingLLMs by Patrick Chao, Alex Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong

A collaborative endeavor, JailbreakingLLMs delves into the intricacies of jailbreaking tailored for LLMs, offering invaluable insights into fortifying these systems against malicious incursions.

4. llm-attacks by Andy Zou, Zifan Wang, and Zico Kolter

The llm-attacks framework presents a comprehensive toolkit for assessing the security posture of LLMs, encompassing a myriad of attack vectors and defensive strategies.

5. PromptInject by Fábio Perez and Ian Ribeiro

PromptInject emerges as a beacon of vigilance against prompt injection attacks, furnishing pragmatic solutions for bolstering the security resilience of LLMs.

6. LLM-Canary by Jamie Cohen and Jackson Gor

LLM-Canary introduces novel methodologies for detecting anomalous behaviors within LLMs, serving as a preemptive safeguard against potential breaches.

7. PyRIT by Microsoft

Microsoft's PyRIT commands attention as a recent addition to the pantheon of LLM pen testing tools, underscoring the burgeoning significance of AI security in the contemporary cybersecurity discourse.

This symphony of innovation owes a debt of gratitude to the concerted efforts of luminaries such as Idan Gelbourt and Simo Jaanus, whose contributions have enriched the discourse on LLM security.

For those inclined towards deeper exploration, our previous research on prompt injection detection awaits:

Link to previous research on prompt injection detection

As custodians of AI security, Knostic remains steadfast in our commitment to illuminating the shadows of cyber threats. Join us on this journey to fortify the defenses of AI systems and navigate the ever-evolving landscape of cybersecurity.