Assail Launches Ares, the First Autonomous Red Team Platform Purpose-Built for the Modern Application Stack

Powered by a proprietary co-evolutionary training architecture, self-evolving AI agents autonomously discover and exploit vulnerabilities across APIs, mobile apps, and web applications — teaching themselves novel attack techniques no human has ever used

BOSTON, March 24, 2026 /PRNewswire/ -- Assail, the company building autonomous AI that hacks applications before adversaries do, today launched Ares™ — the first autonomous red teaming platform engineered exclusively for the modern application stack. Ares deploys purpose-built AI agents that autonomously discover, chain, and exploit vulnerabilities across APIs, mobile applications, and web applications at machine speed, replacing the months-long cycle of traditional red teaming with continuous, on-demand offensive security.

Registrations for Ares now open at ares.assailai.com. Enterprise and business email addresses are required.

Not Built to Do Everything
While other platforms in the emerging autonomous pentesting space attempt to be generalist solutions — spreading thin across networks, infrastructure, cloud, and applications — Ares is purpose-built as a red team platform for what matters most in today's attack surface: the application layer. APIs now account for 70% of global internet traffic and are the primary gateway to enterprise data, yet remain the most undertested attack surface in the enterprise. With AI-generated code accelerating software shipping velocity by over 50%, organizations are deploying vulnerabilities faster than human testers can find them.

"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."

An AI That Teaches Herself to Hack
At the core of Ares is a proprietary co-evolutionary training architecture in which two AI agents — an Adversary Simulator and a Breacher — are locked in perpetual competition on a 24-hour training clock. The Adversary generates increasingly sophisticated security challenges calibrated to the Breacher's current capability frontier; the Breacher attempts to exploit them using integrated security toolchains. Performance feedback drives progressively harder challenges, creating a self-reinforcing arms race that produces continuous capability growth. This architecture is detailed in a published research paper by Assail and is grounded in Group Relative Policy Optimization (GRPO), uncertainty-based frontier filtering, and security-adapted reward formulations.

Critically, Ares does not train on crowdsourced vulnerability data or human-curated attack playbooks — tactics that are already known, already patchable, and already antiquated. Instead, she generates her own synthetic training data through adversarial self-play, teaching herself novel attack chains and TTPs that do not exist in any human knowledge base.

"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."

Ilir Osmanaj, Head of AI Engineering for Assail added, "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."

Self-Healing Agents That Adapt in Real Time
The co-evolutionary pressure produces a self-healing system. When Ares encounters a defense she hasn't seen before — a new WAF rule, an unfamiliar authentication mechanism, an unexpected API response pattern — her agents autonomously adapt their attack strategies in real time without waiting for human intervention, model retraining, or signature updates. This adaptive capability emerges directly from the adversarial training loop, where the Adversary Simulator continuously generates novel defense configurations that force the Breacher to develop bypass strategies on the fly.

Key Platform Capabilities

• Application-Layer Specialization: Purpose-built AI agents for APIs (REST, GraphQL, gRPC, WebSocket), mobile applications (iOS and Android), and web applications. Full coverage of the OWASP API Security Top 10, from Broken Object Level Authorization to Server-Side Request Forgery — not a generalist platform retrofitted for app security.
• Proprietary Frontier Model: Ares is powered by version 1: Dagger, Assail's proprietary 14-billion parameter offensive security model, purpose-trained for vulnerability discovery, exploit chaining, and autonomous attack planning — not a frontier model with a wrapper.
• Co-Evolutionary Self-Play: Continuous adversarial training loop where an Adversary Simulator and Breacher co-evolve through GRPO-based policy optimization, generating novel TTPs without reliance on human-curated data or crowdsourced vulnerability databases.
• Multi-Stage Attack Chain Reasoning: Ares doesn't run scripts — she reasons. Her agents execute multi-step attack chains: fingerprinting the target, enumerating shadow APIs, analyzing token structures, testing authorization boundaries, chaining discovered vulnerabilities, and assessing business impact — mimicking the cognitive workflow of an elite human red team at machine speed.
• Agentic Swarming: Ares deploys up to 100 coordinated agents per target that communicate and share context in real time, covering the entire attack surface simultaneously rather than testing endpoints sequentially.
• Self-Healing Adaptation: When Ares encounters unfamiliar defenses or environmental changes during an engagement, her agents autonomously adapt attack strategies in real time — no human operator, no signature update, no retraining required.
• Continuous Exposure Management: Replaces the point-in-time penetration test with always-on autonomous red teaming that keeps pace with modern CI/CD deployment cycles, shrinking exposure windows from weeks to minutes.

About the Founder
Alissa Knight is a 26-year offensive security veteran, published author, former U.S. intelligence community contractor, and two-time cybersecurity founder with exits. She is the principal architect and developer of the Ares platform, and trained the proprietary Dagger model. Knight is also the author of the published research paper detailing the Ares co-evolutionary framework. She serves as CEO and Chief AI Officer of Assail.

Availability
Ares is available for registration now at ares.assailai.com. Business email addresses are required; personal email domains are not accepted. Assail will be demonstrating Ares at a private launch event during RSA Conference 2026 in San Francisco. To request an invitation or schedule a private demo, visit assail.ai or contact [email protected].

About Assail
Assail is building autonomous offensive AI for the modern application stack. Founded by Alissa Knight and headquartered in Boston, Massachusetts, Assail's flagship platform, Ares, deploys self-evolving AI agents that discover, chain, and exploit vulnerabilities across APIs, mobile apps, and web applications — teaching themselves novel attack techniques through adversarial co-evolution without reliance on human-curated training data. The company is backed by Venture Guides. For more information, visit assailai.com.

SOURCE Assail, Inc.