RECON

Automated Red Teaming for GenAI

Comprehensive scanning of risks and vulnerabilities to ensure the safety and security of GenAI systems, at every step of development.

Deploy LLM applications securely, and with confidence

The use of Large Language Models (LLMs) has the potential to power your enterprise to new heights, but they also need to be implemented securely. Deploying LLMs within your organization exposes new attack vectors like prompt injections, jailbreaks, data leakage and more. These attacks introduce new risk categories across safety, brand reputation and misuse. Furthermore, security teams cannot keep up with the complex and rapidly evolving threats making adoption at scale difficult.

Recon proactively runs automated and comprehensive penetration testing attacks on your LLM powered applications, to help protect you from unique security threats and vulnerabilities, enabling AppSec and product teams to deploy GenAI applications quickly, and with confidence.

Optimize model selection for safety and security

Empower your teams to confidently choose and deploy custom or foundational LLMs within your environment, ensuring they meet the highest standards of safety and security.

Evaluate threat exposure for all GenAI usage, faster

Enable AI Zero Day readiness, by scanning for vulnerabilities across 6 OWASP top 10 for LLMs, with rich insights into the attacks required to compromise your system, reducing the effort of red teamers to comprehensively test your GenAI system.

Continuously improve LLM security

Stay ahead of emerging risks with automated, on-demand red team tests, enabling regular and rigorous threat research of LLM systems to identify vulnerabilities in every version and change made to the LLM endpoint.

Your AppSec super power for automated and secure LLM Red Teaming

Using both an attack library and an LLM agent based solution for red teaming, Recon evaluates the security and safety of GenAI systems.

Automated Red Teaming

Using the comprehensive attack library, run detailed automated LLM attacks from categorized threat profiles including jailbreaks, prompt injection attacks, and input manipulations, which are crucial for maintaining the integrity and security of AI systems.

Human Augmented

Create attack objectives for your LLMs and add goals specific to your business use case, for finance, healthcare, customer service and more, to gain a more focussed simulation of the attack insights that are top-of-the-mind for your business.

Customized attack libraries

Recon comes out-of-the-box with a library of 20K+ known vulnerabilities that can be used to attack GenAI systems for heightened safety and security. This attack library gets updated with new attack techniques and tactics weekly, and also allows you to add your own threat research to ensure your systems are hardened against known potential attacks.

No-Code Integration, Base model agnostic

In less than 5 mins, start scanning your custom endpoints for vulnerabilities, from any base model. Automated scans then run asynchronously, and your teams will be notified once a scan is complete.