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Every Weight Built to Breach

Most "AI-powered" security platforms are thin wrappers around someone else's frontier model. We took the harder path. Dagger is Assail's proprietary 14-billion-parameter model, trained from the ground up for one mission — finding and exploiting vulnerabilities across APIs, web applications, and mobile applications. Every parameter exists to think like an attacker. Not a chatbot with a Burp Suite plugin. A weapon.

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ARES V1.0 DAGGER

The Ares Proprietary Model

Accelerates Exposure Validation and Remediation Through Exploitation

PURPOSE BUILT

SELF LEARNING

VALIDATION

14 Billion Parameters. Zero General-Purpose Compromise.

Dagger is built on a Qwen3-14B foundation, fine-tuned through QLoRA adapters on a curated corpus of offensive security knowledge — OWASP API Security Top 10, OWASP MASTG and MASVS, 340,000+ obfuscation pairs, hardcoded secret detection patterns, and proprietary attack chain data. It runs in BF16 precision with a 32,768-token context window, served on NVIDIA L40S inference infrastructure inside our own Kubernetes cluster. Nine specialized agents — Hermes, Kratos, Enyo, Pallas, Athena, Nemesis, Mnemosyne, and the Polemos orchestrator — call Dagger as their reasoning core. There is no API call leaving our infrastructure to a third-party model provider. There is no shared tenancy with consumer chatbots. There is only Dagger, doing the one job it was built for.

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OWASP API Security Top 10 and OWASP Top 10 coverage across every Ares engagement.

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Of Dagger's training signal comes from customer data. Ever.

PURPOSE BUILT

SELF LEARNING

VALIDATION

14 Billion Parameters. Zero General-Purpose Compromise.

%

OWASP API Security Top 10 and OWASP Top 10 coverage across every Ares engagement.

%

Of Dagger's training signal comes from customer data. Ever.

TEAM

What Ares Tests Across Your APIs

Every vulnerability class in the OWASP API Security Top 10 — discovered, exploited, and validated through real attack paths.

Frontier Models Were Built to Help. Dagger Was Built to Breach.

Frontier models are general-purpose by design. They are tuned to be helpful, harmless, and broadly capable across writing, coding, math, reasoning, and refusal of dangerous requests. That training objective is fundamentally at odds with offensive security work. When a wrapped frontier model encounters a real exploitation task, it hedges. It refuses. It moralizes. It slows down. It costs a fortune per token because it was never optimized for the deterministic, high-volume reasoning that red teaming actually requires. Dagger has no such conflict. It was trained to find weaknesses, chain exploits, synthesize payloads, and validate impact — at machine speed, at machine cost, with zero hedging.

A Model That Knows the Difference Between Theory and Exploit.

A SQL injection on a login form for an internal admin tool is a critical vulnerability. The same SQL injection on a marketing landing page behind three layers of WAF and zero data access may be unexploitable noise. Generic models cannot tell the difference. They flag everything and call it a finding. Dagger was trained with security-adapted reward formulations that account for the non-deterministic, context-dependent nature of real-world exploitation. Validation is not an afterthought added to a chatbot. It is a first-class capability baked into the model. Themis, Aletheia, Argus, and Mnemosyne — the validator agents — call Dagger to confirm that every finding is actually exploitable before it ever reaches a customer report. False positives are not a feature you tolerate. They are an engineering failure you eliminate at the model level.

Continuous Training. Weekly Improvements. Production-Grade Pipeline.

Dagger is not a static asset that ages between releases. Every Sunday at 02:00 UTC, Javelin's automated training pipeline kicks off. New synthetic engagement data is exported. The threshold check confirms a minimum of 100 new training examples. The inference cluster scales down. QLoRA fine-tuning runs at rank 16, alpha 32, learning rate 5e-5 for one epoch. The new adapters merge into the base model. The cluster scales back up. The benchmarking gate enforces a minimum score of 0.80 and rejects any regression greater than 5%. If the new checkpoint clears the gate, it promotes to production. If it doesn't, it rolls back. Customers don't pay for upgrades. They don't wait for product releases. They wake up Monday morning with a sharper Dagger underneath every agent in their tenancy.

Published Research. Not Marketing Vapor.

The co-evolutionary architecture powering Dagger is documented in a peer-reviewed framework paper authored by Assail's research team. Group Relative Policy Optimization. Uncertainty-based frontier filtering. Security-adapted reward formulations. Adversary-Breacher dynamics. These are not slogans. They are architectural decisions, made transparent because the offensive security industry deserves to evaluate the technology it depends on with the same rigor it applies to the systems it defends. Most "AI red teaming" startups will not show you their model architecture because they don't have one. We will show you ours because we built it.

API Red Teaming Without Blind Spots

Ares supports unauthenticated and credentialed API penetration testing using real authentication methods—Bearer tokens, API keys, and user credentials—across public and internal environments. With a lightweight on-prem Docker agent, Ares gains secure access to private networks, exposing internal APIs and attack paths that traditional pentests miss or ignore. No artificial constraints. No visibility gaps. Just continuous, adversary-accurate API testing at scale.

Exploit-Proven Findings, Not Scanner Output

Ares delivers exploit-validated API security reports with complete OWASP API Top 10 coverage, immersive visual attack maps, and full request/response proof for every finding. No false positives. No guesswork. Findings flow directly into Jira, GitHub, GitLab, or email—giving developers everything they need to fix real vulnerabilities fast.

Find Every API—Even the Ones You Didn’t Know Existed

Ares hunts your external and internal environments to uncover every API—documented or not—and automatically registers them as live testing targets. The result is a continuously updated API inventory tied to data sensitivity, compliance exposure, and active security validation, without manual discovery or maintenance.

FEATURES

The Wrapper Problem.

Ask a vendor a single question: "What model is your platform actually running?" If the answer is GPT, Claude, or Gemini — with a custom prompt — you are paying enterprise security pricing for consumer infrastructure that was never designed for the job. Here is what you actually get when you choose a proprietary, purpose-built model over a frontier wrapper.

Economics That Scale With Your Attack Surface

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Wrapped frontier models charge by the token at frontier prices. A real red teaming engagement against a single enterprise API can generate millions of inference calls — recon, fingerprinting, payload synthesis, validation, reporting. At frontier pricing, that math breaks before you finish the first scan. Dagger runs on our infrastructure, at our cost basis, with zero per-token markup passed to you. The result: continuous, exploit-validated red teaming priced like the platform it is — not like a consumer API bill.

Economics That Scale With Your Attack Surface

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Zero Refusals. Zero Moralizing. Zero Wasted Cycles.

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Frontier models are trained to refuse offensive security work. Vendors wrapping them rely on system prompts, jailbreaks, and fragile workarounds that break every time the upstream model updates its safety layer. Dagger was trained for this work. It will fingerprint your stack, enumerate your endpoints, chain your vulnerabilities, and synthesize working exploit payloads without a single refusal, hedge, or "I can't help with that." Because it was never trained to.

Sovereignty Over Your Security Stack

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When you depend on a frontier wrapper, you depend on every decision the upstream provider makes — pricing changes, deprecation timelines, regional availability, content policy revisions, and outages that cascade through every downstream product. Dagger runs in infrastructure we own and control. There is no upstream provider. There is no third-party rate limit. There is no consumer-grade safety filter sitting between you and the work that needs to be done.

Built by Someone Who Has Done the Work

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Dagger was architected by an offensive security practitioner with 26 years in the field, two prior company exits, research cited in U.S. Congressional proceedings, a published book on adversarial security (Hacking Connected Cars, Wiley & Sons), and hacking tools exhibited at The Mob Museum. Wrapper vendors hire prompt engineers. Assail hires hackers. That difference is everywhere in the model — in the reward functions, the training data curation, the validator design, the agent architecture. You cannot prompt-engineer your way to this. You have to build it.

Built by Someone Who Has Done the Work

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Measurable Security Returns

Ares is built to pay for itself—every day you log in. Instead of waiting weeks for a traditional, human-speed penetration test, Ares continuously runs live-fire offensive security across your APIs and mobile apps, validating real exploit paths as your environment changes. This page shows the business impact in plain terms: hours of manual testing eliminated, dollars avoided, and exposure windows reduced—all backed by auditable evidence and automatically generated remediation outputs.

FAQS

Questions? Answers!

Find Some quick answers to the most common questions.

Why did Assail build a custom model instead of using GPT-4, Claude, or Llama?

General-purpose LLMs are trained to refuse offensive security work. Every time you ask one to generate a payload, chain an exploit, or reason about post-exploitation, you're fighting the model's safety training — getting watered-down output, refusals mid-engagement, or hallucinated CVEs that waste your team's time. Ares' proprietary model was purpose-built for offensive operations from the ground up. It doesn't refuse legitimate red team work, it doesn't hallucinate vulnerabilities that don't exist, and it reasons about attack chains the way a senior operator does — because that's the only thing it was trained to do. You're not renting a fraction of a model built for everyone; you're getting a specialist built for one job.

How large is the Ares model, and why 14B parameters instead of a frontier-scale model?

What is Javelin, and why should I care as a customer?

What can Ares do that ChatGPT or Claude simply can't?

How do you prevent Ares from being misused or attacking systems it shouldn't?

Is my data ever used to train Ares' model?

How does Ares compare to Horizon3, XBOW, and human red teamers?

Where can Ares run, and how does deployment work?

Feel free to mail us for any enquiries : orbai@support.com

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