AI-powered application security that prioritizes real risks.

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April 14, 2026

Endor Labs

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AI-powered application security that prioritizes real risks.

Open Source AI Security Risk Prioritization Code Analysis Multi Language Support Vulnerability Remediation

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Overview

AURI is an AI static application security testing (SAST) tool developed by Endor Labs. Acting as security intelligence for AI coding agents, AURI helps teams prioritize real risks, reduce noise and remediate vulnerabilities quickly.

It understands how code works and identifies what matters most to the organisation. The tool intelligently analyses code, triaging false positives and validating multi-file and multi-function data flows.

It assists in identifying complex vulnerabilities beyond traditional rule-based scanning, such as business logic and authentication flaws. AURI integrates directly into AI code editors to give developers and agents the tools to fix code before their first commit.

The software automatically triages findings by parsing syntax, tracing data flow, and reasoning about context and logic to present issues that genuinely matter.

It validates findings, providing transparent evidence and reasoning for every decision. The tool allows the addition of custom prompts and rules to align agent behaviour with an organization's security policies, priorities, and threat models.

Moreover, AURI can highlight complex logic flaws and other risks typically found in pentest reports and bug bounty programs without the need for rule creation or upkeep.

It provides reachable findings and delivers precise, explainable fixes for developers. Additionally, AURI offers smart fix suggestions that generate context-aware solutions aligned with your codebase for review.

It supports over 40 languages, including Java, Javascript, Python, C#, C/C++, Go, Kotlin, TypeScript, Ruby, Rust, JSX, PHP, Scala, Swift, Terraform, and more.

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April 23, 2025

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Pros and Cons

Pros

Prioritizes real risks

Reduces noise

Fast vulnerability remediation

Identifies organizational importance

Analyses multi-file data flows

Triages false positives

Detects complex vulnerabilities

Syntax parsing

Data flow tracing

Logical reasoning capacity

Transparent evidence provision

Custom rules addition

Security policy alignment

Threat modeling

Identifies logical flaws

Pentest report analysis

Bug bounty compatibility

Intelligent fix suggestions

Supports 40+ languages

Generates context-aware solutions

Reachable findings

Delivers precise fixes

Explainable fixes

Customization of prompts

Vulnerability detection before commit

Manual research skipping

Auto-triage of findings

Scalable coverage for risks

Detects risky changes early

LLM security issue identification

High-signal finding focus

Guides on remediation

Verifiable trusted changes

Monitor severity and risk

Multi-file, multi-function analysis

Authentication guard understanding

Framework-specific sanitization

Reduced false positives

Add custom prompts

Java, Javascript, Python support

C#, C/C++, Go support

Kotlin, TypeScript, Ruby support

Rust, JSX, PHP support

Scala, Swift, Terraform support

Business logic flaws detection

Authentication flaws detection

Contextual analysis

View 42 more pros

Cons

Dependency on custom rules

No explicit mention of real-time support

No explicit mobile language support

No explicit built-in threat intelligence

No explicit support for older languages

May miss risks during auto-triage

No explicit hardware security integration

Dependent on code syntax parsing

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Q&A

What is Endor Labs AURI?

Endor Labs AURI is an AI-powered Static Application Security Testing (SAST) tool that provides security intelligence to AI coding agents. It aids teams in overseeing real risks, diminishing noise, and remediating vulnerabilities in a swift manner. AURI comprehends the functioning of code and distinguishes what is substantial for the organization.

How does AURI prioritize real risks?

AURI prioritizes real risks by intelligently analyzing code, differentiating false positives, and validating multi-file and multi-function data flows. It helps to determine complex vulnerabilities beyond conventional rule-based scanning. In addition, it provides reasoning for every decision and presents issues that genuinely matter after tracing data flow, parsing syntax, and reasoning about context and logic.

Can AURI validate multi-file and multi-function data flows?

Yes, AURI is designed to validate multi-file and multi-function data flows. It enables this through its intelligent code analysis feature that allows it to distinguish false positives and complete an extensive validation of data flows.

How does AURI reduce noise in the code?

AURI reduces noise in the code by automatically triaging findings. It does this by parsing syntax, tracing the data flow, and reasoning about context and logic to focus on actual issues that pose real risks and removing irrelevant or less important ones.

How does AURI understand how the code works?

AURI understands how code works by examining the relation between different parts of the code. This involves the analysis of syntax, tracing of data flow, and the reasoning about context and logic. It can also understand the security implications and vulnerabilities inherent in the code.

How does AURI remediate vulnerabilities quickly?

AURI remediates vulnerabilities quickly by integrating directly into AI code editors, providing developers with the tools to fix code even before the first commit. Additionally, it presents smart fix suggestions that generate context-aware solutions relevant to the codebase.

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What type of vulnerabilities can AURI identify?

AURI can identify a wide range of vulnerabilities including, but not limited to, complex vulnerabilities beyond traditional rule-based scanning, business logic, and authentication flaws.

How does AURI integrate with AI code editors?

AURI integrates with AI code editors by being embedded directly within them. This provides developers and agents with the tools to fix code before making their first commit. The integration makes for a streamlined and efficient remediation process.

How does AURI validate its findings?

AURI validates its findings by providing clear evidence and reasoning for each decision. It parses syntax, traces data flow, and reasons about context and logic to validate the significance of its findings.

Can we add custom prompts and rules in AURI?

Yes, in AURI, users have the flexibility to add custom prompts and rules. This is valuable in aligning agent behaviour with the security policies, priorities, and threat models of an organization.

Can AURI align with an organization's security policies and threat models?

Indeed, AURI can align with an organization's security policies and threat models by allowing the addition of custom prompts and rules. This facilitates a uniformity between the tool function and the organization's defined security framework.

Does AURI provide fix suggestions?

Yes, AURI provides smart fix suggestions that generate context-aware solutions aligned with your codebase. These suggestions facilitate quick and effective remediation of identified vulnerabilities.

What languages does AURI support?

AURI supports more than 40 languages including Java, Javascript, Python, C#, C/C++, Go, Kotlin, TypeScript, Ruby, Rust, JSX, PHP, Scala, Swift, Terraform, and others.

How does AURI highlight complex logic flaws?

AURI highlights complex logic flaws by analyzing the code and tracing the data flow. It can detect potential weaknesses and risks typically found in pentest reports and bug bounty programs, without the requirement for rule creation or upkeep.

Which type of flaws can AURI highlight typically found in pentest reports?

AURI can highlight a variety of flaws typically found in pentest reports such as complex logic flaws, broken access controls and other risks. It features a high scalability of coverage across the codebase, making it very effective and efficient.

Can AURI generate context-aware solutions?

Yes, AURI guarantees the generation of context-aware solutions. This is done through its smart fix suggestions feature that takes into account the specific context of your codebase during the generation of solutions.

Does AURI assist in identifying authentication flaws?

Yes, AURI has the capability of identifying authentication flaws as part of its feature set. It can go beyond traditional rule-based scanning to detect complex vulnerabilities like these.

Is it possible to fix code before the first commit with AURI?

Yes, with AURI, it is possible to fix code before the first commit. This is achieved by integrating AURI directly into AI code editors, offering devs and agents the tools they need to rectify any issues in the code before making their initial commit.

Can AURI identify risks with no need for rule creation?

Yes, AURI can identify risks without the need for rule creation or upkeep. It can discover complex logic flaws and other potential risks that are found typically in pentest reports and bug bounty programs.

How does AURI validate and reason through false positives?

AURI validates and reasons through false positives through a rigorous process of parsing syntax, tracing data flow, and reasoning about context and logic. It ensures only genuine issues are highlighted by automatically triaging findings and presenting the ones that are critical.

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