Assail has officially introduced Ares™, a groundbreaking autonomous red teaming platform designed specifically for the modern application stack. With this launch, the company aims to transform how organizations identify and exploit vulnerabilities before real attackers can take advantage of them.
To begin with, Ares leverages purpose-built AI agents that autonomously discover, chain, and exploit vulnerabilities across APIs, mobile applications, and web platforms. Unlike traditional red teaming, which can take months to complete, Ares delivers continuous, on-demand offensive security at machine speed. As a result, organizations can significantly reduce the time required to uncover critical weaknesses in their systems.
Moreover, the platform focuses exclusively on the application layer, which has become the most critical attack surface in today’s digital ecosystem. APIs alone now account for nearly 70% of global internet traffic, making them a primary gateway to sensitive enterprise data. However, they remain one of the most under-tested areas in cybersecurity. At the same time, the rapid adoption of AI-generated code has accelerated software development cycles by over 50%, further increasing the risk of deploying vulnerable applications.
“The cybersecurity industry keeps building platforms that try to boil the ocean — scan the network, scan the cloud, scan the app, and hope the AI figures it out,” said Alissa Knight, Founder and CEO of Assail. “We took the opposite approach. Ares is a specialist. She does one thing — hack APIs, mobile apps, and web apps — and she does it better than any human team or generalist AI platform on the planet.”
At its core, Ares introduces a unique co-evolutionary training architecture. Specifically, two AI agents—an Adversary Simulator and a Breacher—continuously compete in a 24-hour training cycle. While the Adversary generates increasingly complex challenges, the Breacher attempts to exploit them using advanced security toolchains. Consequently, this constant competition drives continuous learning and capability improvement.
In addition, the platform relies on advanced methodologies such as Group Relative Policy Optimization (GRPO), uncertainty-based frontier filtering, and security-focused reward models. Unlike conventional systems, Ares does not depend on crowdsourced vulnerability databases or human-defined attack playbooks. Instead, it generates synthetic training data through adversarial self-play, enabling it to discover entirely new attack techniques and tactics.
“If your AI model only knows what human hackers know, your AI is already behind,” Knight added. “Ares doesn’t learn from humans. She teaches herself. That’s not an incremental improvement — it’s a fundamentally different approach to offensive security.”
Furthermore, Ilir Osmanaj, Head of AI Engineering for Assail highlighted the company’s deep investment in purpose-built AI. “Most companies claiming to use AI for security are running prompts against someone else’s foundation model and calling it a product. We trained Dagger — a 14-billion parameter model built from the ground up for offensive security. Every weight in that model exists to find and exploit vulnerabilities. That level of architectural commitment is what separates a research-grade offensive AI system from a chatbot with a Burp Suite plugin.”
Ultimately, Assail positions Ares as a next-generation solution that redefines offensive cybersecurity. By enabling autonomous, continuous red teaming, the platform empowers organizations to stay ahead of evolving threats and secure their application environments more effectively.
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