Novee v. DIY Generic LLMs

AI models alone don’t reduce risk – only a scalable, sustainable security system does.

Large language models like Claude, GPT, and Gemini can find vulnerabilities quickly, but they weren’t designed to operate a full offensive security workflow. Novee combines frontier AI models with models purpose-trained for offensive security to deliver a complete AI pentesting system.

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Chosen by teams that take attackers seriously

Novee vs. LLMs at a glance

Why Generic LLMs Alone Break in Offensive Security

Frontier models like Claude Mythos can find and exploit software vulnerabilities, but not independently. A general-purpose LLM can find vulnerabilities, but it can’t reliably prove what’s real vs. noise, assign severity based on your business context, scale continuously with predictable cost, or operationalize remediation and retesting.

Alone, generic LLMs for offensive security are limited because:

Can’t reliably prove what’s real

LLMs don’t independently validate findings. The same system that generates a vulnerability is also evaluating it. Outputs are based on inference, not proof, leaving teams to manually validate issues.
Model selection becomes your responsibility

Different stages require different capabilities, and the best model changes constantly, so your team must continuously evaluate, select, and maintain the right model for each task over time.
No path from finding to remediation and retesting

LLMs can surface issues, but confirming exploitability, guiding fixes, and verifying remediation are left entirely to your team, slowing response time and risk reduction.
Severity lacks business context

Generic severity scoring ignores how your application works, forcing teams to manually assess real impact, prioritize issues, and determine which vulnerabilities actually matter to the business and environment.
Unpredictable cost at continuous scale

Token-based pricing makes costs difficult to predict and control, preventing teams from scaling continuous testing across production environments and making it hard to operate a sustainable long-term security program.
No persistent application context

General-purpose AI models reason fresh each session, with no memory of your application, so they can’t accumulate knowledge of roles, workflows, APIs, or access rules, limiting depth and repeatability.

How Novee Turns AI Into Real Risk Reduction

Instead of relying on a single model, Novee uses an Omni-Model Offensive System: it continuously benchmarks and routes each task to the best-performing model, so you’re always using the right capability without having to track or manage it yourself.

Where Novee closes the gap:

Proven exploitability, not assumptions

Novee independently validates every finding using separate agents and deterministic checks, ensuring validation is independent from discovery, so only real, reproducible exploits surface.
The right model, automatically

Novee continuously benchmarks and routes tasks to the best-performing model for each stage, so your team never has to evaluate, select, or maintain models as capabilities and performance shift over time.
Closes the loop from finding to verified remediation

Novee delivers validated findings with tailored remediation and automatic retesting, so vulnerabilities are not just identified but fully resolved, reducing real risk instead of creating operational overhead for teams.
Severity based on real business impact

Novee assigns severity using a deep understanding of your application’s roles, workflows, and data, so teams can prioritize issues based on actual business impact rather than generic vulnerability classifications alone.
Predictable cost at continuous scale

Novee uses per-asset pricing designed for continuous operation, so teams can test as often and as deeply as needed without unpredictable costs, enabling a scalable and sustainable long-term security program.
Persistent context that improves every test

Novee builds a persistent model of each application’s roles, workflows, APIs, and access rules, so every test cycle becomes more targeted, improving coverage, depth, and effectiveness instead of restarting from zero.

Novee vs. LLMs Across Key Areas

Capability	Novee AI Pentesting	General–Purpose LLMs (e.g. Claude Mythos)
Approach	Multi-agent omni-model system built on a persistent application intelligence model. Each stage – mapping, planning, exploitation, validation, remediation – runs on a specialized agent using the best model for that task, including Novee’s own proprietary model trained for offensive security.	A single general-purpose model reasoning fresh every session, optimized for broad language and code-analysis tasks rather than adversarial system interaction.
Where it Excels	Custom applications, business logic flaws, authorization bypasses, chained API weaknesses, multi–step exploit paths in production environments.	Deep source-code analysis of widely-used open-source projects (Linux kernels, browser engines, crypto libraries).
Model Selection	Continuously benchmarks and routes each task to the best-performing model, so you always use the right capability without managing model selection.	Model choice is manual and static. The same model handles every stage, even as better models emerge.
Validation	Independent multi-agent validation with deterministic checks ensures every finding is a proven, reproducible exploit with clear evidence. Every surfaced finding ships with a working exploit, replication steps, and a PoC script.	The same model generates and evaluates findings. Results are based on inference, requiring manual triage to confirm what’s real.
Application Context	Persistent application intelligence captures roles, workflows, APIs, and business logic, improving depth and coverage with every test cycle.	No persistent memory across runs. Each session starts blind, with no accumulated understanding of your application.
Prioritization and Severity	Prioritizes findings based on real business impact, using application context to assess exploitability, access paths, and blast radius.	Assigns generic severity based on vulnerability type, without understanding business context or real-world impact.
Tailored Remediation and Re-testing	Remediation guidance tailored to your WAF, backend, and codebase. Automatic retesting confirms the fix held and catches anything new.	Discovery only. Disclosure, communication, and verification happen elsewhere – fewer than 1% of vulnerabilities found by frontier models in published research have been patched.
Operating Mode + Workflow	Continuous, change-triggered testing on running systems with built-in production safeguards.	Episodic research runs, optimized for static codebases rather than live environments with layered defenses.
Pricing for Continuous Operation	Per-asset pricing designed for continuous testing, with predictable cost regardless of testing depth or frequency.	Usage-based pricing with unpredictable cost, making continuous testing difficult to plan or scale.

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What security leaders say

“As the leading agentic orchestration platform for the enterprise, data isolation between our customers is non-negotiable. We need to prove that continuously, not once a year. Novee adapted to our multi-tenant SaaS product within days.”

Scott Roberts

CISO

“Our pen tests took weeks and consistently missed critical issues. Novee found them immediately and gave us instant remediation guidance. It showed us what we'd been missing.”

John Barrow

CISO

"Traditional DAST produced either zero or irrelevant results. We needed something that could identify complex vulnerabilities like server-side request forgery. Novee consistently surfaces findings we simply weren't seeing before."

Robert Kugler

Head of Security, IT & Compliance

“Novee rethinks penetration testing for how attacks actually happen today. Continuous, attacker-level validation that proves what’s exploitable and shows teams exactly how to fix it is a meaningful shift for modern security programs.”

Troy Wilkinson

Former Fortune 500 CISO

"The hardest vulnerabilities for us to catch aren’t misconfigurations or known patterns. They’re business logic issues that only show up when someone understands how the application is supposed to work. That’s exactly the gap Novee closes."

Tamir Ronen

CISO, HiBob

"We had EASM tools and manual pentests that produced mostly noise. Novee came in black-box with zero credentials and within days found dozens of real vulnerabilities we could actually fix."

Itzik Menashe

CISO, Global VP IT InfoSec & productivity

“As an AI researcher, what stood out about Novee is that they built a proprietary offensive AI model designed to think like an attacker, rather than wrapping generic LLMs. That matters for enterprise-grade results.”

Tal Shapira

PhD, CTO

“This was by far the deepest and fastest security assessment we’ve had. Novee uncovered issues across our web and mobile applications that had gone undetected before, and the level of depth was unlike anything we’d seen from other vendors.”

Amir Tito

CISO

“We had urgent compliance need and we couldn’t wait weeks for DAST findings, an external exposure audit, and an in-depth pentest report. Instead Novee came in and delivered immediate value with their AI pentesting platform; with their findings, we closed our gaps and quickly met the criteria we needed for certification.”

Ron Reiter

CTO

The Novee Advantages

System Behind the Model

The Problem with General–Purpose LLMs:

Pentesting isn't one task. It's mapping an application, planning an attack, executing exploits, validating findings, and guiding remediation. A single general–purpose LLM has to context–switch between all of them with the same prompt and the same tools. No model is best at every stage, and forcing one to be a generalist costs depth at every step. On top of that, the model landscape shifts constantly – and the onus of knowing which model is best, for which task, at any given moment falls entirely on you.

How Novee Improves It:

Novee assigns a dedicated agent to each phase of the assessment. Mapping agents understand application discovery. Planning agents understand attack strategy. Exploitation agents wield specialized skills for SSRF, XSS chains, business logic bypasses, and GraphQL exploitation. Validation agents operate independently of the agents that find vulnerabilities. Each agent runs the best model for its specific task – selected from continuously benchmarked frontier models alongside Novee's own proprietary offensive reasoning model. As better models emerge, Novee automatically promotes the top performer into each stage. You're always running the most effective capability available, without having to think about it.

The result is an autonomous team that mirrors how an elite human pentesting crew operates – at machine speed and scale.

Persistent Intelligence

The Problem with General–Purpose LLMs:

A general–purpose LLM has no memory across sessions. Every run begins blind, with no understanding of your application's roles, permissions, workflows, or access rules. Without that understanding, you're testing blind – pattern–matching against known vulnerability classes instead of finding the flaws that actually matter to your business.

How Novee Improves It:

Every Novee assessment begins with learning, not testing. The platform builds an Asset Intelligence Model (AIM) for each asset: a persistent Application Knowledge Base that ingests API documentation, OpenAPI and Swagger specs, source code where available, and the application itself, exploring every endpoint and page to capture what each entity does, how it relates to others, and what conditions govern access.

No frontier LLM, no matter how advanced, can replicate this, since it's a property of the architecture around the model, not the model itself.

Validated Findings

The Problem with General–Purpose LLMs:

When a single model produces a finding, the only thing validating that finding is the same model that produced it. Inference is not proof. Without independent validation, every result is a hypothesis that engineering teams have to chase down, manually exploit, and confirm before they can act.

How Novee Improves It:

Novee is built to prove, not just detect. Every potential finding passes through a triple–layer validation pipeline before it surfaces. The hacking agent identifies the vulnerability and creates an initial exploit. A second agent independently re–exploits from scratch, with no context from the first, to confirm the result is real and reproducible. A third agent independently checks for false–positive conditions. All three must agree before the finding reaches your team. Where possible, validation is deterministic code rather than LLM inference, confirming exploitability with certainty.

Every finding that surfaces comes with a working exploit, replication steps, and a proof–of–concept script. No manual triage. No second–guessing whether the risk is real.

Severity That Reflects Your Business

The Problem with General-Purpose LLMs:

A vanilla LLM can recognize a vulnerability class and assign textbook severity. It can say, "this is SQL injection, this is high." But severity in practice depends on what the affected component actually does, who can reach it, what data it touches, and what business workflows depend on it. SQL injection on a static marketing page is a fundamentally different risk from SQL injection on the billing API. Without an understanding of your application, every finding gets the same template, and every prioritization decision lands on your team.

How Novee Improves It:

Novee maintains a persistent Asset Intelligence Model, which captures each application's roles, workflows, APIs, access rules, and the relationships between them. Severity is assigned using that context. A finding's risk score reflects the blast radius it actually creates in your environment, weighted against the business logic of the specific application.

The same intelligence is what lets Novee find the high-impact vulnerabilities general-purpose models miss to begin with: business logic flaws, authorization bypasses, and chained API weaknesses that only surface when you understand how a specific application is supposed to work.

Predictable Pricing at Continuous Scale

The Problem with General-Purpose LLMs:

LLM-based testing is most-often priced per token and per usage\. The math becomes brutal when you try to operate continuously. In brute force runs against living production systems, single effective runs can cost as little as $50, but you can't know in advance which run will succeed, leading to costs ballooning exponentially. For a security team that needs to test a portfolio of applications every time code ships, those costs are unpredictable and unbounded.<br />

How Novee Improves It:

Novee is priced per asset. You pay for each asset under test, and within that asset you can run as deep and as often as your security program demands. Continuous testing doesn't grow your bill. Change-triggered runs after every code deploy don't grow your bill. Regression testing across your portfolio doesn't grow your bill.

That pricing model is what makes continuous offensive security testing a sustainable operating discipline rather than a research-grade luxury.

Remediation

The Problem with General–Purpose LLMs:

Finding a vulnerability is necessary but not sufficient. Confirming it's exploitable in your environment, communicating it to the right team, and verifying the fix is a fundamentally different problem. The disclosure–to–remediation gap is so wide that fewer than 1% of vulnerabilities Mythos has found so far have been patched; owing to no fault of the model, simply because the architecture does not enable rapid remediation.

How Novee Improves It:

Every finding includes a working exploit, replication steps, and a proof–of–concept script, so there's no ambiguity about whether the risk is real. Remediation guidance is tailored to your specific architecture – your WAF rules, your codebase – not generic OWASP boilerplate. Once a fix ships, Novee automatically retests to confirm the vulnerability is resolved and checks for any new issues introduced by the change.

Discovery, validation, remediation, and verification, in one loop, on the same cadence as the development teams shipping code.

See Why System > Model

Frontier models show what AI can do. Novee turns that into real risk reduction – continuous, validated, closed–loop offensive security for your production systems.

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