The AI Security Layer
for Mission-Critical Software

Octane is an AI powered offensive security platform that finds exploitable vulnerabilities in mission critical software before attackers do. It traces how functions interact across a codebase, models what an attacker could reach and reports actionable vulnerabilities with evidence, root causes and fix guidance.

Teams can run Octane continuously as code changes or use it for a focused security research engagement against a high value target.

Octane analyzes mission critical software across web applications, backend services, infrastructure software, browser engines, virtualization software and blockchain systems from node clients to smart contracts.

The common thread is consequence. If a vulnerability could expose data, interrupt critical infrastructure or put substantial assets at risk, it is the kind of code Octane was built to analyze.

Octane finds authorization failures, authentication flaws, input validation errors, memory safety bugs, denial of service vulnerabilities, unsafe state transitions, business logic errors and protocol specific vulnerabilities.

Because Octane reasons about what code does rather than relying only on known signatures, it can identify flaws that exist across multiple files, functions and system boundaries.

Yes. Octane has identified vulnerabilities in mature, heavily reviewed software, including browser engines, virtualization software, blockchain clients and server infrastructure. Several findings have received CVE assignments, vendor validation or bug bounty payouts.

Octane can find previously unknown vulnerabilities because it reasons about system behavior and exploitability rather than checking code against a fixed catalog of known flaws.

Octane uses AI agents purpose built for security analysis to trace data flow, model attacker behavior and reason about how functions interact across the full codebase.

Unlike pattern matching scanners, Octane evaluates business logic, system invariants and execution paths. This allows it to find vulnerabilities that require contextual reasoning rather than a known rule or signature.

Octane analyzes execution traces rather than isolated lines or files. Starting from relevant entry points, it follows function calls through internal modules, libraries, dependencies and external boundaries.

This context is essential. Code that appears unsafe in one file may be constrained by an upstream check. Code that appears safe in isolation may become exploitable through another caller, dependency or state transition.

Octane supports large repositories, monorepos, forked codebases and third party dependencies.

An Octane finding includes the root cause, affected code path, exploit scenario, potential impact, severity rationale and remediation guidance. Suggested code changes are included where appropriate.

The goal is to give engineers enough evidence to understand, prioritize and fix the issue without first translating a generic scanner alert into an actual attack path.

For selected findings and researcher-led engagements, Octane can also provide a runnable proof of concept, regression test or researcher authored security report.

Octane validates potential findings against the full execution context of the codebase. It checks whether the suspected vulnerability is reachable, exploitable and material before surfacing it.

Deterministic filters remove impossible findings before AI analysis begins. Specialized detectors focus on individual vulnerability classes, while coherence checks verify that the vulnerability description, root cause and proposed fix match the original code.

Detectors are evaluated against held out test sets, known vulnerable repositories and regression suites before release. Performance is also tested across repeated runs because a detector that produces inconsistent results cannot function as a reliable security control.

Yes. Octane’s security researchers develop and evaluate detectors, investigate false positives and missed findings and provide expert judgment where code alone cannot capture the full business context.

Researchers can also review difficult findings, help teams understand exploit paths and provide targeted remediation guidance. In focused research engagements, Octane researchers direct the analysis, validate findings and develop proofs of concept where warranted.

Octane’s language agnostic security engine supports languages used across application security, infrastructure and blockchain development, including Solidity, Rust, Go, C, C++, C#, Java, Python, TypeScript and Nim.

Its underlying analysis is designed around vulnerability behavior rather than one language’s syntax. This allows Octane to expand into additional languages without rebuilding the entire security architecture.

Yes. Octane analyzes source code directly and does not require production access, production traffic or a live application environment.

Existing tests, accurate dependencies and a buildable repository can improve context and coverage, but a complete test suite is not required. This allows teams to analyze code early in development, before it is deployed or exposed to users.

Most teams can begin reviewing Octane findings within hours. Octane connects to the selected code repository, establishes the analysis scope and runs an initial baseline review.

Once integrated, Octane can analyze subsequent commits and pull requests as the codebase changes. No production credentials or runtime instrumentation are required.

Yes. Octane integrates with source control and CI/CD workflows so security analysis happens inside the development process rather than through a separate reporting system.

Findings can surface in pull requests, trigger security checks before a merge or feed downstream ticketing and remediation workflows. Teams can configure different policies by severity, such as blocking critical findings while treating lower severity issues as warnings.

Yes. Octane reviews AI generated code with the same depth as human written code.

Coding agents increase development speed, but they can also reproduce insecure patterns or introduce subtle authentication, authorization and input validation flaws. Continuous analysis gives teams a security layer that can keep pace with AI assisted development.

Yes. Continuous analysis reviews code as it changes, including pull requests, dependency updates and major upgrades. This helps prevent security posture from deteriorating between formal reviews.

Focused research engagements direct additional compute and researcher attention toward a specific codebase or attack surface. Octane researchers shape the analysis, investigate promising paths and validate material findings through deeper exploitation work.

Pricing depends on the size and complexity of the codebase, analysis cadence, required integrations and level of researcher involvement.

Continuous code analysis is generally structured as a subscription. Focused security research engagements are scoped separately based on the target, required depth and expected deliverables, including proofs of concept or researcher authored reports.

SAST tools match source code against known rules and signatures. They are useful for standardized checks but often generate large volumes of low context alerts and miss vulnerabilities that depend on business logic or interactions across several components.

DAST tools test a running application from the outside. They can identify exposed runtime weaknesses but require a deployed environment and can only test paths their inputs reach.

Octane analyzes source code and traces execution paths across the codebase. It complements these tools by finding contextual and previously unknown vulnerabilities they are not designed to detect.

Penetration tests and manual reviews are limited by time and scope. A skilled security team may go deeply into selected parts of a system, but everything updated after the engagement remains unreviewed until the next one.

Octane applies continuous analysis across the codebase and repeats that analysis as the software changes. Human researchers remain valuable for business context, creative attack design and difficult validation work.

Many teams therefore combine continuous Octane coverage with targeted manual reviews at major release milestones.

Octane supports Ethereum and EVM compatible networks, including Arbitrum, Optimism, Base, Polygon and BNB Chain, as well as Solana, Aptos, Sui and Cosmos based ecosystems. Additional network support is added as customer and research demand evolves.

Coverage extends beyond smart contracts to Layer 2 systems, cross chain bridges, validator software, node clients and consensus implementations.

A traditional smart contract audit is a point in time review of a specific codebase version. Once the report is delivered, new commits, dependency changes and protocol upgrades fall outside that review.

Octane runs continuously across those changes. It helps teams find vulnerabilities during development and maintain the security posture established by formal audits.

Most mature protocols use the two together: human audits at major release milestones and Octane reviewing the code continuously between them.