[{"data":1,"prerenderedAt":812},["ShallowReactive",2],{"/en-us/blog/2025-owasp-top-10-whats-changed-and-why-it-matters":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Fernando Diaz":695,"blog-related-posts-en-us-2025-owasp-top-10-whats-changed-and-why-it-matters":709,"blog-promotions-en-us":750,"next-steps-en-us":802},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":12,"meta":25,"navigation":26,"path":27,"publishedDate":20,"seo":28,"stem":29,"tagSlugs":30,"__hash__":32},"blogPosts/en-us/blog/2025-owasp-top-10-whats-changed-and-why-it-matters.yml","2025 Owasp Top 10 Whats Changed And Why It Matters",[7],"fernando-diaz",null,"security",{"slug":11,"featured":12,"template":13},"2025-owasp-top-10-whats-changed-and-why-it-matters",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"category":9,"tags":21,"body":23},"OWASP Top 10 2025: What's changed and why it matters","Explore new supply chain and error handling risks, ranking shifts, and remediation strategies for all 10 categories.",[18],"Fernando Diaz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1759320418/xjmqcozxzt4frx0hori3.png","2026-01-07",[9,22],"open source","The OWASP Foundation has released the [eighth edition of its influential \"Top 10 Security Risks\" list for 2025](https://owasp.org/Top10/2025/0x00_2025-Introduction/),\nintroducing significant changes that reflect the evolving landscape of application security. Based on analysis\nof more than 175,000 Common Vulnerabilities and Exposures (CVEs) records and feedback from security practitioners across the globe, this update addresses\nmodern attack vectors. Here's everything you need to know about what's changed, why these changes matter,\nand how to protect your systems.\n\n\n> :bulb: Join GitLab Transcend on February 10 to learn how agentic AI transforms software delivery. Hear from customers and discover how to jumpstart your own modernization journey. [Register now.](https://about.gitlab.com/events/transcend/virtual/)\n\n\n## What's new in 2025?\n\nThe shift from 2021 (the last time the list came out) to 2025 represents more than minor adjustments, it's a fundamental shift in application security.\nTwo entirely new categories entered the list and one category was consolidated into another, which highlights emerging risks\nthat traditional testing often misses.\n\nThese additions and shifts can be seen in the chart below:\n\n![OWASP Top 10 - Changes from 2021 to 2025](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767639428/tbekzibeqylorwqrkdau.png)\n\n\n### Two new categories\n\n- **A03: Software Supply Chain Failures**: Expands the 2021 category \"Vulnerable and Outdated Components\" to encompass the entire software supply chain, including dependencies, build systems, and distribution infrastructure. Despite having the fewest occurrences in testing data, this category has the highest average exploit and impact scores from CVEs.\n\n- **A10: Mishandling of Exceptional Conditions**: Focuses on improper error handling, logical errors, and failing open scenarios. This addresses how systems respond to abnormal conditions.\n\n### Major ranking changes\n\n- Security Misconfiguration surged from #5 (2021) to #2 (2025), now affecting 3% of tested applications.\n- Server-Side Request Forgery (SSRF) has been consolidated into A01: Broken Access Control.\n- Cryptographic Failures dropped from #2 to #4.\n- Injection fell from #3 to #5.\n- Insecure Design moved from #4 to #6.\n\n## Why these changes were made\n\nThe OWASP methodology combines data-driven analysis with community insights. The 2025 edition analyzed 589\nCommon Weakness Enumerations (CWEs), which is a substantial increase from the approximately 400 CWEs in 2021.\nThis expansion reflects the growing complexity of modern software systems and the need to capture emerging threats.\n\nThe community survey component addresses a fundamental limitation: testing data essentially looks into the past.\nBy the time security researchers develop testing methodologies and integrate them into automated tools, years may\nhave passed. The two community-voted categories ensure that emerging risks identified by frontline practitioners\nare included, even if they're not yet prevalent in automated testing data.\n\nThe rise of Security Misconfiguration highlights an industry trend toward configuration-based security,\nwhile Software Supply Chain Failures acknowledges the rise of sophisticated attacks targeting compromised packages.\n\n## Using GitLab Ultimate for vulnerability detection and management\n\nGitLab Ultimate provides comprehensive [security scanning](https://docs.gitlab.com/user/application_security/detect/) to detect risks across the\n2025 OWASP Top 10 categories. For instance, the end-to-end platform analyzes your project's source code, dependencies, and infrastructure\ndefinitions. It also uses [Advanced Static Application Security Testing (SAST)](https://docs.gitlab.com/user/application_security/sast/gitlab_advanced_sast/) to detect injection flaws,\ncryptographic failures, and insecure design patterns in source code. [Infrastructure as Code (IaC) scanning](https://docs.gitlab.com/user/application_security/iac_scanning/) finds\nsecurity misconfigurations in your deployment definitions. [Secret Detection](https://docs.gitlab.com/user/application_security/secret_detection/) prevents the leakage of credentials, and\n[Dependency Scanning](https://docs.gitlab.com/user/application_security/dependency_scanning/) uncovers libraries with known vulnerabilities in your software supply chain, which directly\naddresses the new A03 category for Software Supply Chain Failures.\n\nIn addition:\n\n* [Dynamic Application Security Testing (DAST)](https://docs.gitlab.com/user/application_security/dast/) probes your deployed application for broken access control,\nauthentication failures, and injection vulnerabilities by simulating attack vectors.\n* [API Security Testing](https://docs.gitlab.com/user/application_security/api_security/)\nprobes your API endpoints for input validation weaknesses and authentication bypasses.\n* [Web API Fuzz Testing](https://docs.gitlab.com/user/application_security/api_fuzzing/)\nuncovers how your application handles exceptional conditions by generating unexpected inputs, which directly\naddresses the new A10 category for mishandling of exceptional conditions.\n\nSecurity scanning integrates seamlessly into your [CI/CD pipeline](https://about.gitlab.com/topics/ci-cd/), running when code is pushed from a feature\nbranch so developers can remediate vulnerabilities before they reach production. Security findings are consolidated in\nthe [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/), where security\nteams can triage, analyze, and track remediation. GitLab also allows you to leverage AI agents such as [Security Analyst Agent](https://about.gitlab.com/blog/vulnerability-triage-made-simple-with-gitlab-security-analyst-agent/), available in GitLab Duo Agent Platform, to quickly determine what are the most critical vulnerabilities and how to take action on\nthem.\n\nYou can enforce additional controls through [merge request approval policies](https://docs.gitlab.com/user/application_security/policies/merge_request_approval_policies/) and [pipeline execution policies](https://docs.gitlab.com/user/application_security/policies/pipeline_execution_policies/) to ensure security scanning runs consistently across your organization. Customer Success and Professional Services teams at GitLab ensure you derive value from an investment in GitLab in a timely manner.\n\nDeliver secure software faster with security testing in the same platform developers already use.\nTo learn more, visit our [application security testing solutions site](https://about.gitlab.com/solutions/application-security-testing/).\n\n## The OWASP Top 10 2025: Complete breakdown\n\n### A01: Broken Access Control\n\n##### What it is\n\nFailures in enforcing policies that prevent users from acting outside their intended permissions,\nleading to unauthorized access.\n\n##### Impact on your system\n\n- Unauthorized information disclosure\n- Complete data destruction or data modification\n- Privilege escalation (users gaining admin rights)\n- Viewing or editing other users' accounts\n- API access from unauthorized or untrusted sources\n\n##### Notable CWEs\n\n- [CWE-22: Path Traversal](https://cwe.mitre.org/data/definitions/22.html)\n- [CWE-200: Exposure of Sensitive Information to an Unauthorized Actor](https://cwe.mitre.org/data/definitions/200.html)\n- [CWE-352: Cross-Site Request Forgery (CSRF)](https://cwe.mitre.org/data/definitions/352.html)\n\n### A02: Security Misconfiguration\n\n##### What it is\n\nSystems, applications, or cloud services configured incorrectly from a security perspective.\n\n##### Impact on your system\n\n- Exposure of sensitive information through error messages\n- Unauthorized access through default accounts\n- Unnecessary services or features enabled\n- Outdated security patches\n- Server does not send security headers or directives\n\n##### Notable CWEs\n\n- [CWE-16: Configuration](https://cwe.mitre.org/data/definitions/16.html)\n- [CWE-521: Weak Password Requirements](https://cwe.mitre.org/data/definitions/521.html)\n- [CWE-798: Use of Hard-coded Credentials](https://cwe.mitre.org/data/definitions/798.html)\n\n### A03: Software Supply Chain Failures\n\n##### What it is\n\nBreakdowns or compromises in building, distributing, or updating software through vulnerabilities or malicious changes in dependencies, tools, or build processes.\n\n##### Impact on your system:\n\n- Compromised packages introducing backdoors\n- Malicious code injected during build processes\n- Vulnerable dependencies cascading through your application\n- Use of components from untrusted sources in production\n- Changes within your supply chain are not tracked\n\n##### Notable CWEs\n\n- [CWE-1395: Dependency on Vulnerable Third-Party Component](https://cwe.mitre.org/data/definitions/1395.html)\n- [CWE-1104: Use of Unmaintained Third Party Components](https://cwe.mitre.org/data/definitions/1104.html)\n\n### A04: Cryptographic Failures\n\n##### What it is\n\nFailures related to lack of cryptography, insufficiently strong cryptography, leaking of cryptographic keys, and related errors.\n\n##### Impact on your system:\n\n- Sensitive data exposure (passwords, credit cards, health records)\n- Man-in-the-middle attacks\n- Data breach through weak encryption\n- Key compromise leading to system-wide exposure\n- Regulatory compliance failures (GDPR, PCI DSS)\n\n##### Notable CWEs\n\n- [CWE-327: Use of a Broken or Risky Cryptographic Algorithm](https://cwe.mitre.org/data/definitions/327.html)\n- [CWE-330: Use of Insufficiently Random Values](https://cwe.mitre.org/data/definitions/330.html)\n\n### A05: Injection\n\n##### What it is\n\nSystem flaws allowing attackers to insert malicious code or commands (SQL, NoSQL, OS commands, LDAP, etc.) into programs.\n\n##### Impact on your system\n\n- Data loss or corruption through SQL injection\n- Complete database compromise\n- Server takeover through command injection\n- Cross-site scripting (XSS) attacks\n- Information disclosure\n- Denial of service\n\n##### Notable CWEs\n\n- [CWE-89: SQL Injection](https://cwe.mitre.org/data/definitions/89.html)\n- [CWE-78: OS Command Injection](https://cwe.mitre.org/data/definitions/78.html)\n\n### A06: Insecure Design\n\n##### What it is\n\nWeaknesses in design representing different failures, expressed as missing or ineffective control design—architectural flaws rather than implementation bugs.\n\n##### Impact on your system\n\n- Weak password reset flows\n- Missing authorization steps\n- Flawed business logic allowing bypasses\n- Inadequate threat modeling leading to blind spots\n- Design patterns that fail under attack scenarios\n\n##### Notable CWEs\n\n- [CWE-209: Generation of Error Messages Containing Sensitive Information](https://cwe.mitre.org/data/definitions/209.html)\n- [CWE-522: Insufficiently Protected Credentials](https://cwe.mitre.org/data/definitions/522.html)\n- [CWE-656: Reliance on Security Through Obscurity](https://cwe.mitre.org/data/definitions/656.html)\n\n### A07: Authentication Failures\n\n##### What it is\n\nVulnerabilities allowing attackers to trick systems into recognizing invalid or incorrect users as legitimate.\n\n##### Impact on your system\n\n- Account takeover and credential stuffing\n- Session hijacking\n- Brute force attacks succeeding\n- Weak password recovery mechanisms exploited\n- Multi-factor authentication bypass\n\n##### Notable CWEs\n\n- [CWE-287: Improper Authentication](https://cwe.mitre.org/data/definitions/287.html)\n- [CWE-306: Missing Authentication for Critical Function](https://cwe.mitre.org/data/definitions/306.html)\n- [CWE-521: Weak Password Requirements](https://cwe.mitre.org/data/definitions/521.html)\n\n### A08: Software or Data Integrity Failures\n\n##### What it is\n\nCode and infrastructure failing to protect against invalid or untrusted code/data being treated as trusted and valid.\n\n##### Impact on your system\n\n- Unsigned updates allowing malicious code injection\n- Insecure deserialization leading to remote code execution\n- CI/CD pipeline compromise\n- Auto-update mechanisms exploited\n- Tampered software artifacts\n\n##### Notable CWEs\n\n- [CWE-345: Insufficient Verification of Data Authenticity](https://cwe.mitre.org/data/definitions/345.html)\n- [CWE-346: Origin Validation Error](https://cwe.mitre.org/data/definitions/346.html)\n- [CWE-347: Improper Verification of Cryptographic Signature](https://cwe.mitre.org/data/definitions/347.html)\n\n### A09: Security Logging & Alerting Failures\n\n##### What it is\n\nInsufficient logging and monitoring with inadequate alerting, which makes rapid response difficult.\n\n##### Impact on your system\n\n- Attacks go undetected for extended periods\n- Breach investigation becomes impossible\n- Compliance violations from lack of audit trails\n- Delayed incident response\n- Inability to determine scope of compromise\n\n##### Notable CWEs\n\n- [CWE-117: Improper Output Neutralization for Logs](https://cwe.mitre.org/data/definitions/117.html)\n- [CWE-532: Insertion of Sensitive Information into Log File](https://cwe.mitre.org/data/definitions/532.html)\n- [CWE-778: Insufficient Logging](https://cwe.mitre.org/data/definitions/778.html)\n\n### A10: Mishandling of Exceptional Conditions\n\n##### What it is\n\nPrograms failing to prevent, detect, and respond to unusual and unpredictable situations, which leads to crashes, unexpected behavior, or vulnerabilities.\n\n##### Impact on your system\n\n- Information disclosure through verbose error messages\n- Denial of service from unhandled exceptions\n- State corruption from improper error handling\n- Race conditions exploited\n- Systems failing open instead of closed\n- Application crashes exposing sensitive data\n\n##### Notable CWEs\n\n- [CWE-248: Uncaught Exception](https://cwe.mitre.org/data/definitions/248.html)\n- [CWE-390: Detection of Error Condition Without Action](https://cwe.mitre.org/data/definitions/390.html)\n- [CWE-391: Unchecked Error Condition](https://cwe.mitre.org/data/definitions/391.html)\n\n## Prevention and remediation best practices\n\nGitLab provides tools to enable you to not only quickly find and remediate vulnerabilities within the OWASP Top 10,\nbut also to prevent them from making it into your production system. By following these best practices you can enhance\nand maintain your security posture:\n\n#### Automated security scanning for all repositories\n\n- Perform [SAST Scanning](https://docs.gitlab.com/user/application_security/sast/) to detect insecure design patterns like plaintext password storage, inadequate error handling, and missing encryption during code review, catching design flaws early in the development lifecycle.\n- Perform [Secret Detection](https://docs.gitlab.com/user/application_security/secret_detection/) to identify credentials in configuration files, environment variables, and code, preventing plaintext password storage and ensuring secrets are properly managed through GitLab's CI/CD variables with masking and encryption.\n- Perform [DAST Scanning](https://docs.gitlab.com/user/application_security/dast/) to detect broken access control vulnerabilities\n- Perform [Dependency Scanning](https://docs.gitlab.com/user/application_security/dependency_scanning/) to scan project dependencies against vulnerability databases, identifying known CVEs in direct and transitive dependencies across multiple package managers (npm, pip, Maven, etc.).\n- Perform [Container Scanning](https://docs.gitlab.com/user/application_security/container_scanning/) to analyze Docker images for vulnerable base layers and packages, ensuring container supply chain security before deployment.\n- Perform [IaC Scanning](https://docs.gitlab.com/user/application_security/iac_scanning/) to check your infrastructure definition files for known vulnerabilities.\n- Leverage [API Security Tools](https://docs.gitlab.com/user/application_security/api_security/) to secure and protect web APIs from unauthorized access, misuse, and attacks.\n- Perform [Web API Fuzz Testing](https://docs.gitlab.com/user/application_security/api_fuzzing/) to discover bugs and potential vulnerabilities that other QA processes might miss.\n\n![Security Results in MR](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767639431/zs6xh8hz6mud3vuig3dy.png)\n\u003Cp>\u003C/p>\n\u003Ccenter>\u003Ci>View vulnerabilities detected in MR with diff from feature branch to main branch.\u003C/i>\u003C/center>\n\n#### Understand your security posture\n\n- Generate a [software bill of materials (SBOM)](https://docs.gitlab.com/user/application_security/dependency_list/) for complete dependency visibility and compliance requirements.\n- Leverage the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/) to sort through and triage vulnerabilites via consolidated view of security vulnerabilities found in your codebase.\n- Quickly take action on vulnerabilities using [detailed remdiation guidance](https://docs.gitlab.com/user/application_security/vulnerabilities/) and [risk assessment data](https://docs.gitlab.com/user/application_security/vulnerabilities/risk_assessment_data/).\n- Use [Security Iventory](https://docs.gitlab.com/user/application_security/security_inventory/) to visualize which assets you need to secure and understand the actions you need to take to improve security.\n- Leverage [Compliance Center](https://docs.gitlab.com/user/compliance/compliance_center/) to manage compliance standards adherence reporting, violations reporting, and compliance frameworks.\n\n![Security Inventory](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767639429/e9vnakc8yiyjbjm8aj7s.png)\n\u003Cp>\u003C/p>\n\u003Ccenter>\u003Ci>Use Security Inventory to viewing enabled security scanners and vulnerabilities.\u003C/i>\u003C/center>\n\n#### Set up prevention and maintain documentation\n\n- Configure [Security Policies](https://docs.gitlab.com/user/application_security/policies/) to block merges or deployments when high-severity vulnerabilities are detected in dependencies, enforcing security standards automatically.\n- Use [Compliance Frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) to enforce organizational security standards through automated policy checks that verify encryption requirements, credential management practices, and secure workflow implementations are followed.\n- Use GitLab Wiki and repository documentation to maintain security design principles, approved patterns, and architectural decision records that guide developers toward [secure-by-design implementations](https://about.gitlab.com/blog/last-year-we-signed-the-secure-by-design-pledge-heres-our-progress/).\n- Implement merge request approval rules requiring security architect review for features involving authentication, authorization, encryption, or sensitive data handling, ensuring design-level security validation.\n- Create tests to verify input validation and allowlist approaches for file paths\n- Use GitLab Issues and Epics to document security requirements and threat models during the design phase, creating a traceable record of security decisions and ensuring security considerations are addressed before implementation begins.\n\n![Security Policy Dashboard](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767639429/q4eelq3rqt0oonzhwoyb.png)\n\u003Ccenter>\u003Ci>View and set Security Policies scoped to instance, group, or project.\u003C/i>\u003C/center>\n\n#### Leverage AI\n\n- Use [Code Suggestions](https://docs.gitlab.com/user/project/repository/code_suggestions/) for proactive guidance during development, suggesting secure design patterns like proper password hashing (bcrypt, Argon2), encrypted storage mechanisms, and appropriate error handling that doesn't leak sensitive information.\n- Use [Security Analyst Agent](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/security_analyst_agent/) to review detected insecure design vulnerabilities in context, explaining the architectural implications, assessing risk based on your application's threat model, and providing remediation strategies that address root design flaws rather than just symptoms.\n- [Review your code using AI](https://docs.gitlab.com/user/project/merge_requests/duo_in_merge_requests/#have-gitlab-duo-review-your-code) to help ensure consistent code review standards in your project.\n\n![GitLab Security Analyst Agent](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767639430/kqvgagepwleabt5zdkco.png)\n\u003Cp>\u003C/p>\n\u003Ccenter>\u003Ci>Leverage Security Analyst Agent to quickly triage and assess security vulnerabilities.\u003C/i>\u003C/center>\n\n## Key takeaways for development teams\n\n- **Supply chain security is critical**: With A03's addition and high-impact scores, securing your software supply chain is no longer optional. Implement SBOM tracking, dependency scanning, and integrity verification throughout your pipeline.\n- **Configuration matters more than ever**: The rise to #2 shows that configuration-based security is now a primary attack vector. Automate configuration verification and implement IaC with security baked in.\n- **Traditional threats persist**: While Injection and Cryptographic Failures dropped in ranking, they remain critical. Don't deprioritize them just because they've fallen on the list.\n- **Error handling is security**: The new A10 category emphasizes that how your application handles failures is a security concern. Implement secure error handling from the start.\n- **Testing must evolve**: The expanded CWE coverage (589 vs. 400 in 2021) means testing strategies must be comprehensive. 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your pipeline for AI-discovered zero-days","AI is finding vulnerabilities faster than teams can patch. Learn how pipeline enforcement, automated triage, and AI remediation close the gap.",[715],"Omer Azaria","2026-04-20","Anthropic's [Mythos Preview model](https://red.anthropic.com/2026/mythos-preview/) recently identified thousands of zero-day vulnerabilities across every major operating system and web browser, including an OpenBSD bug that went undetected for 27 years. In testing, Mythos autonomously chained four vulnerabilities into a working browser exploit that escaped its sandbox. Anthropic is restricting access to Mythos, but the company’s head of offensive cyber research expects threats to have comparable tooling within six to twelve months.\n\nThe defender side of the equation hasn't kept pace. One third of exploited Common Vulnerabilities and Exposures (CVEs) in the first half of 2025 showed activity on or before disclosure day, before most teams even know there's something to patch. AI is compressing that window further, accelerating attackers and flooding teams with whitehat disclosures faster than they can triage. Defender tooling has improved, but most organizations can't operationalize it fast enough to close the gap between discovery and exploitation.\n\nWhen the window between disclosure and exploitation is measured in hours, the security team can't be the last line of defense. Security has to run where code enters the system: in the pipeline, on every merge request, enforced by policy. The fixes that can be automated should be. The ones that can't need to reach the right human faster than they do today.\n\n## Known vulnerabilities are already outpacing remediation\n\nThe bottleneck isn't detection, it's acting at scale on what teams already know. Sixty percent of breaches in the 2025 Verizon DBIR involved exploiting known vulnerabilities where a patch was already available. Teams couldn’t close them in time.\n\nThe backlog was untenable before Mythos. Developers spend [11 hours per month remediating vulnerabilities](https://about.gitlab.com/resources/developer-survey/) post-release instead of shipping new work. Over half of organizations have at least one open internet-facing vulnerability, and the median time to close half of those is 361 days. Exploitation takes hours, while remediation takes months.\n\nAI-assisted development is widening the gap, and stakeholders know it. By June 2025, AI-generated code was adding over 10,000 new security findings per month across Fortune 50 repositories, a 10x jump from six months earlier. Georgia Tech identified 34 [CVEs attributable to AI-generated code](https://research.gatech.edu/bad-vibes-ai-generated-code-vulnerable-researchers-warn) in March 2026, up from 6 in January, and that count reflects only the ones where AI authorship is clear. AI coding assistants hallucinate package names, reach for outdated patterns, and copy insecure examples from training data. More code, more dependencies, and more vulnerabilities per line are generated faster than security teams can review them.\n\nDefenders need to harness frontier AI models, too — not bolted onto the SDLC as external tooling, but running inside the same policies, approvals, and audit trail as the rest of the team. \n\n## Security at the speed of AI coding\n\nWhen a critical CVE drops, how quickly can your team confirm which projects are affected? How many tools does an alert cross before a developer can submit a fix?\n\nThe teams that benefit most from AI already have policies, enforcement, and controls embedded in their development workflows. AI amplifies that foundation. It doesn't replace it.\n\n**Enforcement at the point of change.** As exploitation windows compress, every line of code entering a repository needs to pass through a defined set of controls. Not a separate review, in a different tool, by a different team. Organizations need the ability to enforce security policies across every group and project, with the merge request as the enforcement point. Policies defined once, applied everywhere, with exceptions reviewed, approved, and logged.\n\n**Simple issues caught before the merge request, not during.** Hardcoded secrets, known-vulnerable imports, and deprecated API calls can be flagged in the IDE before a developer pushes a commit. Catching them at authoring time means fewer findings blocking the MR, so review cycles go to the findings that require cross-component context: reachability, exploitability, and architectural risk.\n\n**Triage automated by default, not by exception.** Embedding security into every merge request creates a volume problem. More scans, more findings, more noise reaching developers who aren’t trained to distinguish a reachable critical from a theoretical one. AI must handle false positive detection, reachability, exploitability context, and severity assessment before a developer sees the finding, so the findings they see actually warrant their time.\n\n**Remediation governed like any other change.** AI-based remediation compresses the timeline for closing vulnerabilities, but every generated fix must move through the same governance as a human-authored change: policies enforce scans, the right reviewers approve, and evidence is recorded. GitLab’s automated remediation capability proposes each fix in a merge request with a confidence score. The MR records which policy applied, which scans ran, what they found, and who approved. Human code and AI-generated code move through the same process, with the same audit trail.\n\n## What a ready pipeline looks like\n\nHere's how these pieces work together when a high-severity vulnerability is discovered and the clock is running.\n\nA proof-of-concept exploit for a vulnerability in a popular open-source package appears on a security mailing list. There’s no CVE, no National Vulnerability Database (NVD) entry, and no scanner signature yet. The security team finds out the usual way: someone shares it in Slack.\n\nA security engineer asks the security agent if the package is in use, which projects have affected versions, and whether any vulnerable call paths are reachable in production. The agent checks the dependency graph for every project, matches the affected versions and entry points from the disclosure, and returns a ranked list of exposed projects with details about reachability. There’s no need to search through repositories by hand or wait for a scanner update. The question, \"Are we exposed?\" is answered in minutes.\n\nThe engineer starts a remediation campaign for every exposed project. The remediation agent suggests fixes: version updates where a patched release is available, and targeted call-path patches where it is not. Scan execution policies are already in place for projects tagged SOC 2. The engineer hardens the rules to block merges on any merge request that introduces or keeps the affected dependency, and an approval policy now requires security sign-off on every fix. The agent's first proposed patch fails the pipeline when an integration test catches a regression. The agent revises the patch based on the test failure, and the second attempt passes. Developers review the changes, security signs off under the stricter policy, and merges proceed across the campaign.\n\nAt the next audit review, the security team presents a report showing how policies were enforced and risks were reduced during the campaign. It includes scan results, policies applied, approvers, and merge timestamps for every MR in every affected project. The evidence was automatically generated in flight, not assembled after the fact.\n\n## Close the gaps now\n\nMythos exists today, and comparable models will be in attacker hands within a year. Every month between now and then is a chance to strengthen your software supply chain.\n\nAsk these questions about your pipeline:\n\n* How do you enforce that security scans run on every merge request, not just the projects where teams configured them?\n\n* If a compromised package entered your dependency tree today, would your pipeline catch it before build?\n\n* When a scanner flags a critical finding, how many tool boundaries does it cross before a developer starts the fix?\n\n* If an AI agent proposed a code fix for a vulnerability, what process would that fix go through before reaching production, and is that process auditable?\n\n* When auditors ask for evidence that a specific policy was enforced on a specific change, how long does it take to produce?\n\nIf the answers expose gaps, address them now. [Talk to a GitLab solutions architect](https://about.gitlab.com/sales/) about the role of security governance in your development lifecycle.",[719,9,528],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":26,"template":13,"slug":722},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":724,"config":736},{"title":725,"description":726,"authors":727,"heroImage":729,"date":730,"category":9,"tags":731,"body":735},"Manage vulnerability noise at scale with auto-dismiss policies","Learn how to cut through scanner noise and focus on the vulnerabilities that matter most with GitLab security, including use cases and templates.",[728],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,732,560,733,734],"tutorial","features","product","Security scanners are essential, but not every finding requires action. Test code, vendored dependencies, generated files, and known false positives create noise that buries the vulnerabilities that actually matter. Security teams waste hours manually dismissing the same irrelevant findings across projects and pipelines. They experience slower triage, alert fatigue, and developer friction that undermines adoption of security scanning itself.\n\nGitLab's auto-dismiss vulnerability policies let you codify your triage decisions once and apply them automatically on every default-branch pipeline. Define criteria based on file path, directory, or vulnerability identifier (CVE, CWE), choose a dismissal reason, and let GitLab handle the rest.\n\n## Why auto-dismiss?\nAuto-dismiss vulnerability policies enable security teams to:\n- **Eliminate triage noise**: Automatically dismiss findings in test code, vendored dependencies, and generated files.\n- **Enforce decisions at scale**: Apply policies centrally to dismiss known false positives across your entire organization.\n- **Maintain audit transparency**: Every auto-dismissed finding includes a documented reason and links back to the policy that triggered it.\n- **Preserve the record**: Unlike scanner exclusions, dismissed vulnerabilities remain in your report, so you can revisit decisions if conditions change.\n\n## How auto-dismiss policies work\n\n1. **Define your policy** in a vulnerability management policy YAML file. Specify match criteria (file path, directory, or identifier) and a dismissal reason.\n\n2. **Merge and activate.** Create the policy via **Secure > Policies > New  policy > Vulnerability management policy**. Merge the MR to enable it.\n3. **Run your pipeline.** On every default-branch pipeline, matching vulnerabilities are automatically set to \"Dismissed\" with the specified reason. Up to 1,000 vulnerabilities are processed per run.\n4. **Measure the impact.** Filter your vulnerability report by status \"Dismissed\" to see exactly what was cleaned up and validate that the right findings are being handled.\n\n## Use cases with ready-to-use configurations\n\nEach example below includes a policy configuration you can copy, customize, and apply immediately.\n\n### 1. Dismiss test code vulnerabilities\n\nSAST and dependency scanners flag hardcoded credentials, insecure fixtures, and dev-only dependencies in test directories. These are not production risks.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss test code vulnerabilities\"\n    description: \"Auto-dismiss findings in test directories\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"test/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"tests/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"spec/**/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"__tests__/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: used_in_tests\n\n```\n\n### 2. Dismiss vendored and third-party code\n\nVulnerabilities in `vendor/`, `third_party/`, or checked-in `node_modules` are managed upstream and not actionable for your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss vendored dependency findings\"\n    description: \"Findings in vendored code are managed upstream\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendor/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"third_party/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendored/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 3. Dismiss known false positive CVEs\n\nCertain CVEs are repeatedly flagged but don't apply to your usage context. Teams dismiss these manually every time they appear. Replace the example CVEs below with your own.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss known false positive CVEs\"\n    description: \"CVEs confirmed as false positives for our environment\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-44487\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2024-29041\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-26136\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: false_positive\n\n```\n\n### 4. Dismiss generated and auto-created code\n\nProtobuf, gRPC, OpenAPI generators, and ORM scaffolding tools produce files with flagged patterns that cannot be patched by your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss generated code findings\"\n    description: \"Generated files are not authored by us\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"generated/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.pb.go\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.generated.*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 5. Dismiss infrastructure-mitigated vulnerabilities\n\nVulnerability classes like XSS (CWE-79) or SQL injection (CWE-89) that are already addressed by WAF rules or runtime protection. Only use this when mitigating controls are verified and consistently enforced.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss CWEs mitigated by WAF\"\n    description: \"XSS and SQLi mitigated by WAF rules\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-79\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-89\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: mitigating_control\n\n```\n\n### 6. Dismiss CVE families across your organization\n\nA wave of related CVEs for a widely-used library your team has assessed? Apply at the group level to dismiss them across dozens of projects. The wildcard pattern (e.g., `CVE-2021-44*`) matches all CVEs with that prefix.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Accept risk for log4j CVE family\"\n    description: \"Log4j CVEs mitigated by version pinning and WAF\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2021-44*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: acceptable_risk\n\n```\n\n## Quick reference\n\n| Parameter | Details |\n|-----------|---------|\n| **Criteria types** | `file_path` (glob patterns, e.g., `test/**/*`), `directory` (e.g., `vendor/*`), `identifier` (CVE/CWE with wildcards, e.g., `CVE-2023-*`) |\n| **Dismissal reasons** | `acceptable_risk`, `false_positive`, `mitigating_control`, `used_in_tests`, `not_applicable` |\n| **Criteria logic** | Multiple criteria within a rule = AND (must match all). Multiple rules within a policy = OR (match any). |\n| **Limits** | 3 criteria per rule, 5 rules per policy, 5 policies per security policy project. Vulnerabilty management policy actions process 1000 vulnerabilities per pipeline run in the target project, until all matching vulnerabilities are processed. |\n| **Affected statuses** | Needs triage, Confirmed |\n| **Scope** | Project-level or group-level (group-level applies across all projects) |\n\n## Getting started\nHere's how to get started with auto-dismiss policies:\n\n1. **Identify the noise.** Open your vulnerability report and sort by \"Needs triage.\" Look for patterns: test files, vendored code, the same CVE across projects.\n\n2. **Pick a scenario.** Start with whichever use case above accounts for the most findings.\n\n3. **Record your baseline.** Note the number of \"Needs triage\" vulnerabilities before creating a policy.\n\n4. **Create and enable.** Navigate to **Secure > Policies > New policy > Vulnerability management policy**. Paste the configuration from the use case above, then merge the MR.\n\n5. **Validate results.** After the next default-branch pipeline, filter by status \"Dismissed\" to confirm the right findings were handled.\n\nFor full configuration details, see the [vulnerability management policy documentation](https://docs.gitlab.com/user/application_security/policies/vulnerability_management_policy/#auto-dismiss-policies).\n\n> Ready to take control of vulnerability noise? [Start a free GitLab Ultimate trial](https://about.gitlab.com/free-trial/) and configure your first auto-dismiss policy today.\n",{"slug":737,"featured":26,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":739,"config":748},{"title":740,"description":741,"authors":742,"heroImage":744,"date":745,"body":746,"category":9,"tags":747},"GitLab 18.10 brings AI-native triage and remediation ","Learn about GitLab Duo Agent Platform capabilities that cut noise, surface real vulnerabilities, and turn findings into proposed fixes.",[743],"Alisa Ho","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773843921/rm35fx4gylrsu9alf2fx.png","2026-03-19","GitLab 18.10 introduces new AI-powered security capabilities focused on improving the quality and speed of vulnerability management. Together, these features can help reduce the time developers spend investigating false positives and bring automated remediation directly into their workflow, so they can fix vulnerabilities without needing to be security experts.\n\nHere is what’s new:\n\n* [**Static Application Security Testing (SAST) false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/false_positive_detection/) **is now generally available.** This flow uses an LLM for agentic reasoning to determine the likelihood that a vulnerability is a false positive or not, so security and development teams can focus on remediating critical vulnerabilities first.  \n* [**Agentic SAST vulnerability resolution**](https://docs.gitlab.com/user/application_security/vulnerabilities/agentic_vulnerability_resolution/) **is now in beta.** Agentic SAST vulnerability resolution automatically creates a merge request with a proposed fix for verified SAST vulnerabilities, which can shorten time to remediation and reduce the need for deep security expertise.  \n* [**Secret false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/secret_false_positive_detection/) **is now in beta.** This flow brings the same AI-powered noise reduction to secret detection, flagging dummy and test secrets to save review effort.\n\nThese flows are available to GitLab Ultimate customers using GitLab Duo Agent Platform. \n\n## Cut triage time with SAST false positive detection\n\nTraditional SAST scanners flag every suspicious code pattern they find, regardless of whether code paths are reachable or frameworks already handle the risk. Without runtime context, they cannot distinguish a real vulnerability from safe code that just looks dangerous.\n\nThis means developers could spend hours investigating findings that turn out to be false positives. Over time, that can erode confidence in the report and slow down the teams responsible for fixing real risks.\n\nAfter each SAST scan, GitLab Duo Agent Platform automatically analyzes new critical and high severity findings and attaches:\n\n* A confidence score indicating how likely the finding is to be a false positive  \n* An AI-generated explanation describing the reasoning  \n* A visual badge that makes “Likely false positive” versus “Likely real” easy to scan in the UI\n\nThese findings appear in the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/), as shown below. You can filter the report to focus on findings marked as “Not false positive” so teams can spend their time addressing real vulnerabilities instead of sifting through noise.\n\n![Vulnerability report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1773844787/i0eod01p7gawflllkgsr.png)\n\n\nGitLab Duo Agent Platform's assessment is a recommendation. You stay in control of every false positive to determine if it is valid, and you can audit the agent's reasoning at any time to build confidence in the model. \n\n\n## Turn vulnerabilities into automated fixes\n\nKnowing that a vulnerability is real is only half the work.  Remediation still requires understanding the code path, writing a safe patch, and making sure nothing else breaks.\n\nIf the vulnerability is identified as likely not be a false positive by the SAST false positive detection flow, the Agentic SAST vulnerability resolution flow automatically:\n\n1. Reads the vulnerable code and surrounding context from your repository  \n2. Generates high-quality proposed fixes  \n3. Validates fixes through automated testing   \n4. Opens a merge request with a proposed fix that includes:  \n   * Concrete code changes  \n   * A confidence score  \n   * An explanation of what changed and why\n\nIn this demo, you’ll see how GitLab can automatically take a SAST vulnerability all the way from detection to a ready-to-review merge request. Watch how the agent reads the code, generates and validates a fix, and opens an MR with clear, explainable changes so developers can remediate faster without being security experts.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1174573325?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"GitLab 18.10 AI SAST False Positive Auto Remediation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\nAs with any AI-generated suggestion, you should review the proposed merge request carefully before merging.\n\n## Surface real secrets\n\nSecret detection is only useful if teams trust the results. When reports are full of test credentials, placeholder values, and example tokens, developers may waste time reviewing noise instead of fixing real exposures. That can slow remediation and decrease confidence in the scan.\n\nSecret false positive detection helps teams focus on the secrets that matter so they can reduce risk faster. When it runs on the default branch, it will automatically:\n\n1. Analyze each finding to spot likely test credentials, example values, and dummy secrets  \n2. Assign a confidence score for whether the finding is a real risk or a likely false positive  \n3. Generate an explanation for why the secret is being treated as real or noise  \n4. Add a badge in the Vulnerability Report so developers can see the status at a glance\n\nDevelopers can also trigger this analysis manually from the Vulnerability Report by selecting **“Check for false positive”** on any secret detection finding, helping them clear out findings that do not pose risk and focus on real secrets sooner.\n\n## Try AI-powered security today\n\nGitLab 18.10 introduces capabilities that cover the full vulnerability workflow, from cutting false positive noise in SAST and secret detection to automatically generating merge requests with proposed fixes.\n\nTo see how AI-powered security can help cut review time and turn findings into ready-to-merge fixes, [start a free trial of GitLab Duo Agent Platform today](https://about.gitlab.com/gitlab-duo-agent-platform/?utm_medium=blog&utm_source=blog&utm_campaign=eg_global_x_x_security_en_).",[734,9,733],{"featured":12,"template":13,"slug":749},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":751},[752,766,777,788],{"id":753,"categories":754,"header":756,"text":757,"button":758,"image":763},"ai-modernization",[755],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":759,"config":760},"Get your AI maturity score",{"href":761,"dataGaName":762,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":764},{"src":765},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":767,"categories":768,"header":769,"text":757,"button":770,"image":774},"devops-modernization",[734,563],"Are you just managing tools or shipping innovation?",{"text":771,"config":772},"Get your DevOps maturity score",{"href":773,"dataGaName":762,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":775},{"src":776},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":778,"categories":779,"header":780,"text":757,"button":781,"image":785},"security-modernization",[9],"Are you trading speed for security?",{"text":782,"config":783},"Get your security maturity score",{"href":784,"dataGaName":762,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":786},{"src":787},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":789,"paths":790,"header":793,"text":794,"button":795,"image":800},"github-azure-migration",[791,792],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":796,"config":797},"See how GitLab compares to GitHub",{"href":798,"dataGaName":799,"dataGaLocation":237},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":801},{"src":776},{"header":803,"blurb":804,"button":805,"secondaryButton":810},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":806,"config":807},"Get your free trial",{"href":808,"dataGaName":44,"dataGaLocation":809},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":811},{"href":48,"dataGaName":49,"dataGaLocation":809},1777302582614]