Reading view

AWS Security Agent full repository code scanning feature now available in preview

Today, we’re excited to announce the preview release of full repository code review, a new capability in AWS Security Agent that performs deep, context-aware security analysis of your entire code base. AI-driven cybersecurity capabilities are advancing rapidly. AWS Security Agent can now find vulnerabilities and build working exploits across your entire code base at a scale and speed we haven’t seen before, reasoning like a human security researcher, but operating at machine velocity. Unlike traditional static analysis tools that match code against known vulnerability patterns, full repository code review reasons about your application’s architecture, trust boundaries, and data flows the way a human security researcher would and then produces developer-ready findings with transparent evidence and concrete remediation.

AWS is prioritizing free early access for customers, giving defenders the opportunity to strengthen their code bases and share what they learn so the whole industry can benefit.

The challenge: Security analysis that scales with your code

Development teams today face persistent tension. Traditional static application security testing (SAST) tools are fast and reliable at catching known patterns such as a SQL injection sink, an unescaped output, or a hard-coded credential. But modern applications are complex systems of services, APIs, trust boundaries, and authorization logic. The most dangerous vulnerabilities often aren’t single-line pattern violations, rather they’re systemic gaps where a validation function covers four of five cases, one endpoint is missing the authorization annotation its neighbors have, or encoding is applied in one context but not another.

Manual security reviews catch these issues, but they’re expensive, slow, and don’t scale to the pace of modern development. As code bases grow, teams are forced to choose between breadth and depth.

Full repository code review is built to close this gap. It gives your team an automated security researcher that reads and reasons about your entire repository, not just individual lines or file, and surfaces findings that pattern-matching tools miss.

How it works: Profile, search, triage, validate

Full repository code review operates in four stages that mirror how an experienced security engineer conducts an engagement.

  1. Profile the application: The scanner begins by reading the entire repository and building a security model of the application including entry points, trust boundaries, data flows, authorization invariants, and the defenses already in place. This profiling step accounts for every source file, so coverage decisions are explicit rather than implicit. The result is a structured understanding of what the application does and where its attack surface lies.

  2. Search for vulnerabilities: An orchestrator reads the security profile, reasons about the attack surface, and dispatches specialized agents to the highest-risk components. Each agent receives a scoped assignment with specific modules, threat context, and adversarial questions. Agents are free to follow imports and callers beyond their starting scope when a lead takes them there.

  3. Triage and deduplicate: Candidate findings are deduplicated (same sink, same root cause) and low-confidence noise is filtered out before the validation phase.

  4. Validate independently: For every candidate, an independent validator re-reads the source code and traces the full attack chain. The validator argues both sides: it looks for reasons the finding might not be a vulnerability (compensating controls, intentional design), and it looks for reasons it is one (alternative attack paths, edge cases). A finding is only rejected when the evidence against it is as strong as the evidence that promoted it. This process produces findings with structured Verified and Could not verify sections, so your team knows exactly what the scanner confirmed in the code and what depends on your deployment environment.

What makes this different

Full repository code review differs from traditional static analysis in two fundamental ways. It reasons about your application’s actual behavior rather than matching against known vulnerability patterns, and it presents findings with structured evidence that makes uncertainty explicit rather than hidden.

Context-aware reasoning, not pattern matching

Because the scanner builds a security model before searching for vulnerabilities, it reasons about the application’s actual behavior, not only surface-level code patterns.

Consider a real example: A stored procedure had a SQL injection vulnerability. A traditional SAST tool would flag the specific EXECUTE IMMEDIATE call. The scanner went deeper and it identified that the central validation function doesn’t block single quotes in any of its five regex profiles, listed all five profiles by name, explained why single quotes matter for the specific database engine, and noted that another stored procedure skips the validation function entirely. Instead of a point fix on one call site, the finding led to a comprehensive remediation of the systemic gap.

In another case, the scanner found an XSS vulnerability where a value was added to a field without HTML encoding. The same value was properly encoded with Encode.forHtml() in a different context within the same file. Pattern-matching tools miss this because the encoding function is present, but the vulnerability is the inconsistency, which requires understanding the application’s behavior across code paths.

Validated findings with transparent uncertainty

Every finding is structured for efficient developer triage:

  • Problem: What the code does wrong, with specific file and line references.
  • Impact: What an attacker gains, with details about deployment context.
  • Verified and could not verify: What the scanner confirmed directly in code versus what depends on your environment (network segmentation, runtime behavior).
  • Remediation: Concrete fix suggestions with specific code changes, not generic guidance.
  • Severity and confidence: Calibrated independently. Severity reflects the impact if the vulnerability is exploitable; confidence reflects how much of the attack chain was verified in code.

How full repository code review fits into your workflow

Full repository code review is designed to complement, not replace, your existing security tooling. Here’s how it fits into a modern development workflow:

  • Before security reviews: Run a full repository code review before scheduling a penetration test or security review. The review surfaces the obvious and semi-obvious issues so your security team can focus their limited time on the subtle, design-level questions that require human judgment.
  • When onboarding acquired or open source code: Full repository code review is especially valuable when your team inherits code through acquisitions or vendor dependencies, or from open source components you’re integrating. The scanner builds a security model from scratch, so it doesn’t need institutional knowledge of the codebase.
  • During architecture reviews: Because the scanner reasons about trust boundaries, data flows, and authorization invariants, its findings often surface architectural issues, not only implementation bugs. Review the scan results alongside your threat models to validate assumptions about how components interact.

Follow our Quickstart guide to set up and execute a full repo code review with AWS Security Agent.

Preview availability and pricing

Full repository code review is available today in preview at no additional charge for AWS Security Agent customers. During the preview, we welcome your feedback as we refine the experience. Use the built-in feedback mechanism in the Security Agent web application or reach out to your AWS account team.

Get started today

Visit the AWS Security Agent console to enable full repository code review and run your first scan. For more information, see the AWS Security Agent documentation.

Ayush Singh

Ayush Singh

Ayush is a Senior Product Manager at AWS, where he leads the development of AWS Security Agent. Ayush has a proven record of scaling enterprise-grade, open source, and agentic AI products. He is dedicated to building tools that empower organizations to effectively scale their security practices. Ayush holds an MBA from the University of Rochester and a B.Tech in Computer Science from KIIT University.

Daniele Bonadiman

Daniele is a Senior Applied Scientist at AWS, where he works on AWS Security Agent. Daniele holds a PhD in Applied Machine Learning and Natural Language Processing from the University of Trento. During his time at AWS, Daniele has contributed to several AI initiatives focusing on conversational AI, multi-agent systems orchestration and code interpretation for AI agents.

  •  

AWS Security Agent on-demand penetration testing now generally available

AWS Security Agent on-demand penetration testing is now generally available, enabling you to run comprehensive security tests across all your applications, not only your most critical ones. This milestone transforms penetration testing from a periodic bottleneck into an on-demand capability that scales with your development velocity across AWS, Azure, GCP, other cloud-providers, and on-premises. With multicloud support, AWS Security Agent allows you to consolidate penetration testing across your entire infrastructure.

AWS Security Agent delivers autonomous penetration testing that operates 24/7 at a fraction of the cost than manual penetration tests. Most organizations limit manual penetration testing to their most critical applications and conduct these tests periodically due to time and cost limitations. This approach can leave the majority of their application portfolio exposed to vulnerabilities in the periods between tests. Security Agent allows you to increase the penetration testing speed, frequency, and coverage across all applications, not just your top critical applications. The Security Agent approach compresses the penetration testing timeline from weeks to days, dramatically reducing your exposure window while maintaining development velocity.

In preview, HENNGE K.K. shared, “AWS Security Agent delivered valuable insights that enhance the robustness of HENNGE’s products and services—insights we hadn’t discovered through manual testing. The contextually aware agentic AI approach provides different insights than traditional methods, while surfacing valuable application improvements beyond pure security findings. This allows us to rapidly accelerate our security lifecycle, reducing the typical testing duration by more than 90%.”

How on-demand penetration testing works

AWS Security Agent represents a new class of frontier agents – autonomous systems that work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently without constant human oversight. It deploys specialized AI agents to help discover, validate, and report security vulnerabilities through sophisticated multi-step attack scenarios. Unlike traditional scanners that generate findings without validation, AWS Security Agent operates penetration tester, helping to identify potential vulnerabilities, then attempting to exploit them with targeted payloads and attack chains to confirm they are legitimate security risks.

Consider the example of deploying a new payment processing feature. With traditional penetration testing, you would either delay the release until the next scheduled assessment, which could be weeks or months away, or you would deploy with uncertainty. With AWS Security Agent, you initiate a penetration test in minutes and receive validated findings within hours, as shown in the following figure, which you can use to help identify and remediate critical vulnerabilities before they reach production and deploy more confidently.

Figure 1: Initiate a pen test in minutes and receive validated findings within hours.

Figure 1: Initiate a pen test in minutes and receive validated findings within hours.

This validation approach helps minimize false positives and provides visibility into the agent’s reasoning. The agent shows how it plans attacks, what payloads it uses, the tools it builds to execute exploits, and how it verifies successful exploitation, providing transparency. Each finding includes Common Vulnerability Scoring System (CVSS) risk scores, application-specific severity ratings, and detailed reproduction steps, as shown in the following figure, so your team can focus on confirmed vulnerabilities rather than investigating scanner noise.

Figure 2: Each finding includes Common Vulnerability Scoring System (CVSS) risk scores, application-specific severity ratings, and detailed reproduction steps

Figure 2: Each finding includes Common Vulnerability Scoring System (CVSS) risk scores, application-specific severity ratings, and detailed reproduction steps.

How AWS Security Agent delivers proactive, context-aware application security

AWS Security Agent combines static application security testing (SAST), dynamic application security testing (DAST), and penetration testing into a single context-aware agent. The agent ingests design documents, architecture diagrams, infrastructure-as-code, source code, user stories, and threat models to understand how you designed, built, and deployed your application. It then identifies how individual vulnerabilities connect into higher-severity attack chains. Richer context produces higher-quality findings and more actionable remediation recommendations.

Consider three findings detected by AWS Security Agent

These might have been discovered by other tools, but if they were detected, they might not have been properly prioritized in isolation because those tools lack contextual awareness of how the application was designed, built, and used, and how each finding is being used as part of an attack chain. These findings exist in custom code written by the customer, so there might not be a Common Vulnerabilities and Exposures (CVE) vulnerability to detect in all cases, making these zero-day vulnerability discoveries.

  • Finding1 – Stored cross-site scripting (XSS) (CVSS 6.1, Medium): A threat actor could inject a script into a comment field that captures an admin’s session cookie inside standard HTTPS traffic. SAST and DAST might flag this as an isolated, medium-severity input validation issue competing with hundreds of other findings in a backlog. Without application context, these tools struggle to recognize it as the entry point to a critical chain.
  • Finding 2 – Session hijack through admin access (unscored): A threat actor could use a hijacked admin session to reach restricted endpoints. This step goes undetected by every tool category, SAST analyzes code not runtime sessions, DAST crawls as a standard user, Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) see a valid HTTPS session hidden among other valid traffic. No finding is generated and no alerts are fired.
  • Finding 3 – Database credential exfiltration through admin config endpoint (CVSS 9.8, Critical — never discovered):The admin panel exposes an /admin/config endpoint returning environment variables, including the production database connection string with credentials in plaintext. A threat actor could use a hijacked admin session to call this endpoint, extract credentials, connect directly to the production database containing customer personally identifiable information (PII), and exfiltrate the full customer dataset. SAST doesn’t flag this because the code functions as designed. DAST never reaches the admin panel. EDR and XDR see a legitimate authenticated API call.

AWS Security Agent connects the dots

By ingesting the source code and documentation, AWS Security Agent identifies that the /admin/config endpoint exposes database credentials, something SAST, DAST, or a Software Composition Analysis (SCA) scanner would struggle to identify. SAST and DAST might flag Finding 1 as isolated and medium severity without recognizing it as the entry point to a critical chain. Finding 2 goes undetected, because SAST analyzes code not sessions, DAST crawls as a standard user, EDR and XDR see valid HTTPS traffic hidden among other valid traffic. Finding 3 goes unflagged because SAST doesn’t flag endpoints functioning as designed, DAST never reaches the admin panel, and EDR and XDR see legitimate API calls. The program requirements document (PRD) provided the key context that the endpoint was designed for legitimate troubleshooting but assumed the authentication gateway would prevent unauthorized access. AWS Security Agent chains all three findings, tests each step, and proves the full attack works.

The chain is the vulnerability

A deprioritized CVSS 6.1 XSS unlocks a critical CVSS 9.8 database credential exfiltration through steps that are beyond the capabilities of most automated SAST or DAST tools. Finding 1 sits in a backlog for weeks, contributing to alert fatigue for developers and security teams. Finding 2 generates no finding. Finding3 functions as designed. Developers fix critical and high findings first, but a CVSS 6.1 among hundreds of others waits, leaving customers exposed. AWS Security Agent, informed with application context, elevates the entire sequence from three isolated findings to a critical, validated vulnerability. This means deeper insights and recommended remediations, fewer false positives, and more cost-effective penetration testing. Security teams can now confidently scale continuous penetration testing across a wider application portfolio than is being tested today, or isn’t being tested as frequently while it’s being developed. AWS Security Agent delivers these insights and recommended remediations within hours instead of weeks, helping customers ship more secured applications faster.

Scout24 confirmed the value of these capabilities directly:
“AWS Security Agent outperformed traditional DAST by combining agentic testing with source-code context to deliver code-aware application security testing. It identified a critical publicly exploitable issue that other approaches had not surfaced. Its transparent reasoning gave us confidence in the attack paths explored and the coverage achieved.” – Abdul Al-Kibbe, Tech Lead, Security, Scout24 SE

Bamboo Health validated the depth of context-aware discovery in their own usage:
“AWS Security Agent surfaced findings that no other tool has uncovered by truly understanding the application, its code, and connecting that context to what it discovered during testing. Legacy scanners simply could not match what Security Agent revealed. It gave us visibility into issues we typically would not see, even from human pentesting teams. For the first time, it felt like I had an AI tool on my side as a defender.” – Travis Allen, Manager of Security Operations at Bamboo Health

Set up your first penetration test

Setting up a penetration test is straightforward and includes the following steps.

Create agent spaces as logical boundaries
Start by creating an agent space for each application or project. An agent space serves as a logical boundary where you can connect documents and code repositories for your application. The agent uses this application context from your documentation and code to create targeted test cases specific to your implementation. For example, create an Ecommerce Platform agent space and connect your GitHub repository, API specifications, and architecture documentation. By analyzing these materials to understand how your application handles payment processing, sessions, and user stories, the agent creates targeted test cases for payment manipulation vulnerabilities and session hijacking risks specific to your implementation.

Complete domain ownership validation
Before testing begins, complete domain ownership validation by adding a DNS TXT record or uploading a verification file to your domain. This required step helps ensure you have authorization to test the target application, protecting both you and AWS. Complete this one-time setup for all domains during initial configuration to prevent delays when running penetration tests.

Add application context for improved accuracy and deeper findings
While optional, providing source code and documentation considerably improves testing accuracy. Include the following:

  • Source code: Enables white-box testing to identify implementation-specific vulnerabilities. You can directly connect your source code repositories with GitHub integration
  • API specifications (OpenAPI or Swagger): Documents all endpoints, parameters, and authentication requirements so the agent tests comprehensively rather than discovering endpoints through trial and error
  • Architecture documentation: Helps the agent understand service interactions and potential attack chains
  • Product requirements documents: Helps the agent understand purpose, features, functionality, and user stories when using the application
  • Existing threat models: Guides the agent to focus on your highest-risk areas and known concerns

Test across any environment
AWS Security Agent operates across AWS, Azure, GCP, other cloud-providers, and on-premises. Configure public endpoints directly by providing the URL, or connect private endpoints through a VPC for secure testing of internal applications without internet exposure. By using this multicloud support, you can consolidate security testing across your entire infrastructure using AWS Security Agent.

Testing authenticated applications

Most vulnerabilities exist in authenticated areas of applications, and you can’t find them without signing in. Configure multiple credentials for different user roles to enable comprehensive testing:

  • Standard user credentials for customer-facing functionality
  • Privileged user credentials for administrative functions
  • Service account credentials for API-to-API authentication
  • Multi-factor authentication (MFA), including MFA and two-factor authentication (2FA) and

By testing with multiple credential sets, AWS Security Agent identifies privilege escalation vulnerabilities, for example, discovering that a standard user can access admin functions by manipulating API parameters.

Provide sign-in guidance for complex authentication

AWS Security Agent uses large language model (LLM)-based sign-in capabilities to navigate authentication flows including OAuth, SAML, Okta, and MFA. Providing clear sign-in guidance significantly improves success rates, for example:

  1. Navigate to app.example.com/auth/login
  2. Enter username in the Email or Username field
  3. Enter password in the Password field
  4. Choose Sign In

Specific instructions, sequential steps, and success verification criteria help the agent navigate your authentication flow reliably.

From findings to fixes: The complete security lifecycle

Review the findings and take actions to remediate with AWS Security Agent.

Validated, actionable findings
AWS Security Agent validates potential vulnerabilities by attempting exploitation to confirm the security risk exists. This validation approach minimizes false positives and reduces the time your team spends manually verifying findings, so you can focus on legitimate vulnerabilities that require fixes. The agent achieved a 92.5% success rate on CVE Bench v2.0, demonstrating its capability to discover and validate real-world vulnerabilities.

Each finding includes CVSS risk scores, application-specific severity ratings, detailed reproduction steps, and impact analysis that explains the business risk. In an ecommerce application, the agent might discover a price manipulation vulnerability and show that attackers can modify product prices during checkout, allowing customers to obtain items for free and directly impacting revenue.

The agent also identifies how vulnerabilities chain together, showing how lower-severity findings can become gateways to critical exploits.

You can export reports as PDF files for executive reporting, compliance documentation, developer handoffs, and audit trails.

Remediation with code fix suggestion completes the process
Traditional penetration testing ends with a report, then weeks or months pass while developers research, implement, and deploy fixes. AWS Security Agent completes the security lifecycle:

  1. Run penetration test and identify confirmed vulnerabilities
  2. Review findings and prioritize critical issues
  3. Trigger remediation to generate pull requests with code fixes
  4. Developer review and merge the ready-to-implement fixes in hours instead of days
  5. Retest to confirm vulnerabilities are resolved
  6. Deploy with confidence knowing security issues are addressed

Get started with on-demand penetration testing

AWS Security Agent on-demand penetration testing is now available in the following AWS Regions:

  • US East (N. Virginia)
  • US West (Oregon)
  • Europe (Ireland)
  • Europe (Frankfurt)
  • Asia Pacific (Sydney)
  • Asia Pacific (Tokyo)

Pricing

Pricing is straightforward and transparent: $50 per task-hour, metered per second. A task-hour represents the time AWS Security Agent actively works on testing your application. Based on current metrics, an average application test takes approximately 24 task-hours, resulting in a typical cost of $1,200 per comprehensive penetration test and remediation. See the AWS Security Agent pricing page for more details on pricing and free trial.

Sample bill:

  • Small web application (8 task-hours): $400
  • Medium ecommerce platform (24 task-hours): $1,200
  • Large enterprise application (48 task-hours): $2,400

Note: The preceding sample bills and estimated costs are for illustrative purposes only and are not guaranteed. Actual costs can vary based on multiple factors including application complexity, number of endpoints, authentication mechanisms, code base size, and the depth of testing required. Your actual penetration test duration and cost will depend on your specific application characteristics.

By using AWS Security Agent, you can perform penetration testing more quickly, frequently, and on more of your applications at a reduced cost. For example, some customers found that AWS Security Agent provided up to 70–90% savings compared to traditional manual penetration testing approaches.

Ready to transform your security testing?
Create your first agent space and run your first penetration test in minutes:

  1. Visit the AWS Management Console for AWS Security Agent
  2. Create an agent space for your application
  3. Complete domain ownership validation
  4. Connect your code repository and add documentation
  5. Configure and run your first penetration test

Conclusion

By using on-demand penetration testing in AWS Security Agent, you can test all your applications continuously, not just your most critical ones periodically. Start with one application, experience validated findings and automatic remediation in action, then scale comprehensive security testing across your entire portfolio.

Visit AWS Security Agent to begin testing today.

Ayush Singh

Ayush Singh

Ayush is a Senior Product Manager at AWS, where he leads the development of AWS Security Agent. Ayush has a proven record of scaling enterprise-grade, open source, and agentic AI products. He is dedicated to building tools that empower organizations to effectively scale their security practices. Ayush holds an MBA from the University of Rochester and a B.Tech in Computer Science from KIIT University.

Christopher Rae

Christopher Rae

Christopher is a Principal Worldwide Security Specialist and the AI Security GTM Lead at AWS, defining go-to-market strategy for securing AI workloads, AI-powered security capabilities, and resilience to evolving AI-powered threats. He evangelizes secure-by-design and defense-in-depth solutions to accelerate secure AI adoption. He earned his MBA from UC San Diego and BA from University of Maine. In his free time, he enjoys epicurean travel, hockey, skiing, and discovering new music.

  •  
❌