secret-scanning

5 posts

gitlab

Secure every commit to production with Claude and GitLab (opens in new tab)

Claude’s security tools can identify and fix vulnerabilities during coding, but they do not govern everything that happens afterward. The post argues that GitLab should secure the remaining path to production through enforced scans, approval policies, data controls, lifecycle-wide coverage, and audit evidence. Together, Claude handles in-session authoring while GitLab provides organization-wide governance. ## From In-Session Findings to Enforced Controls - GitLab applies security configuration profiles across projects and pipelines, preventing teams from bypassing required scans. - Merge request approval policies enforce separation of duties, ensuring an agent or developer cannot approve and merge its own changes. - Critical vulnerabilities can block merges until a designated approver reviews them. - Vulnerability reports and security dashboards permanently track whether findings were detected, dismissed, or resolved. ## Turning Security Activity into Audit Evidence - Compliance controls require scans to run on every merge request and expose findings for human review. - Pipeline logs, approval records, and audit events provide reproducible evidence of what was scanned and who approved each change. - Compliance frameworks such as SOC 2, PCI DSS, and FedRAMP can map evidence to named requirements. - Compliance status reports show which controls have passed, failed, or remain pending, including activity involving agents. ## Controlling Sensitive Data Sent to Models - Context exclusions prevent secrets, proprietary files, and regulated data from being sent to AI models. - Self-managed GitLab environments and self-hosted models can keep code and inference within an organization’s boundary. - Teams can select approved models, restrict model access, and prevent code from being used for training. - GitLab Duo prompt guardrails detect secrets and limit the content available to prompts, reducing prompt-injection risk. ## Scanning Beyond the Coding Session - Session-based reviews cannot detect vulnerabilities introduced after code is written, such as newly disclosed dependency flaws like Log4Shell. - GitLab provides dependency, container, infrastructure-as-code, secret, and DAST scanning across the delivery lifecycle. - Security Review Flow uses reasoning to identify business-logic flaws, authorization errors, and race conditions that deterministic scanners may miss. - Advanced SAST produces reproducible, CWE-mapped results, providing more consistent evidence than potentially variable LLM-based findings. ## Consistent Coverage for Humans and Agents - Claude’s plugin primarily reviews code written and committed within its session. - Developer shell commands, including Claude’s `!` shell escape, may fall outside that review scope. - Claude Security can review broader codebases when explicitly invoked by a developer or administrator. - GitLab pipeline scans and merge request policies apply to every change, regardless of whether it was written by a human or an agent. GitLab’s policies and lifecycle-wide scanning provide the durable controls needed after an AI coding session ends. Teams can use Claude for immediate security feedback while relying on GitLab to enforce consistent review, protect sensitive data, block unsafe releases, and maintain audit-ready evidence through production.

github

How exposed is your code? Find out in minutes—for free (opens in new tab)

GitHub’s Code Security Risk Assessment offers organizations a free, one-click way to uncover vulnerabilities that manual reviews and limited scanning tools may miss. Using CodeQL, it scans up to 20 active repositories and provides a prioritized view of risks by severity, language, repository, and vulnerability type. The assessment also connects findings to GitHub Code Security and Copilot Autofix, helping teams move from discovery to remediation. ## What the Assessment Reveals - Scans up to 20 of an organization’s most active repositories. - Reports vulnerabilities by severity: - Critical - High - Medium - Low - Breaks findings down by programming language. - Identifies the specific security rules and vulnerability classes detected. - Highlights the repositories with the greatest number of vulnerabilities. - Shows which findings are eligible for Copilot Autofix. - Uses CodeQL, GitHub’s static analysis engine. The assessment is free for organization administrators and security managers using GitHub Enterprise Cloud or GitHub Team. Scanning does not consume the organization’s GitHub Actions minutes quota. ## Combining Code and Secret Exposure The Code Security Risk Assessment complements GitHub’s existing Secret Risk Assessment. - Both assessments can be launched from one entry point. - A tabbed interface separates secret exposure findings from code vulnerabilities. - The combined view helps organizations understand their overall security posture. - Secret Protection focuses on preventing credential leaks. - Code Security identifies and helps remediate vulnerabilities in source code. GitHub cites 2025 usage figures for Secret Protection, including nearly 2 billion scanned pushes and 19 million blocked secret exposures. ## From Detection to Remediation Finding vulnerabilities is only the first step; reducing risk requires fixing them. - GitHub reports that Copilot Autofix resolved 460,258 security alerts in 2025. - Half of vulnerability alerts were fixed directly in pull requests. - Mean remediation time was 0.66 hours with Copilot Autofix, compared with 1.29 hours for manual fixes. - Assessment results show how many discovered vulnerabilities could potentially be fixed automatically. - Organizations can enable GitHub Code Security directly from the results page. The assessment is intended for organizations with no current scanning, teams evaluating existing tools, or security leaders seeking broader visibility across repositories. Running it provides a quick, no-cost baseline for prioritizing security work.

github

Investing in the people shaping open source and securing the future together (opens in new tab)

Open source security depends on supporting the maintainers who sustain critical software, not merely hosting their code. GitHub argues that funding, education, practical security tools, and AI assistance can reduce maintainer burnout while improving the broader software supply chain. Its new commitments focus on making security work more manageable as AI accelerates both vulnerability discovery and attacks. ## A $12.5 Million Open Source Security Commitment - GitHub is joining Anthropic, AWS, Google, and OpenAI in committing $12.5 million to the Linux Foundation’s Alpha-Omega initiative. - The funding will help integrate emerging AI security capabilities into existing open source workflows. - The effort builds on GitHub’s broader role as a provider of security tools, education, and long-term maintainer support. ## Expanding Maintainer Resources - More than 280,000 GitHub maintainers are eligible for free access to: - Core GitHub services - GitHub Copilot Pro - GitHub Actions - Code scanning and Autofix - Secret scanning and push protection - Dependency alerts - GitHub’s Secure Open Source Fund is adding $5.5 million in Azure credits and funding for training, expertise, community support, and new partners such as Datadog, Open WebUI, the Atlantic Council, and OWASP. - GitHub Security Lab is improving security advisories and Private Vulnerability Reporting to reduce low-quality reports and ease the burden on maintainers. ## Results from Security-Focused Funding - Previous Secure Open Source Fund programs supported 138 projects and more than 200 maintainers across 38 countries. - Participating projects produced: - 191 new CVEs - More than 250 prevented secret leaks - More than 600 detected and resolved leaked secrets - These projects collectively affect billions of monthly software downloads. - GitHub concludes that security improves when maintainers receive dedicated time, funding, education, and tools that fit naturally into their workflows. ## Using AI to Reduce Maintainer Burden - AI has increased the speed and scale of vulnerability discovery for both attackers and defenders. - Maintainers are facing more automated pull requests and security reports, often with poor signal-to-noise ratios, contributing to burnout. - GitHub’s goal is to use AI for triage, pull request review, vulnerability identification, and remediation—not simply to generate more findings. - GitHub has open sourced an AI-powered security research framework so maintainers, rather than only specialized security teams, can benefit from it. - Copilot Pro provides eligible maintainers with AI-assisted code review, agentic security remediation workflows, and access to multiple leading models. GitHub’s overall recommendation is to treat AI as a force multiplier and pair it with sustained funding, education, and workflow-integrated security tools. Supporting maintainers directly is presented as the most effective way to protect the wider software ecosystem.

github

What’s new with GitHub Copilot coding agent (opens in new tab)

GitHub Copilot coding agent is becoming more capable at handling delegated development work from issue to pull request. Recent updates let users choose models, receive self-reviewed and security-checked changes, apply team-specific workflows through custom agents, and move tasks between the cloud and local CLI without losing context. Together, these features aim to reduce cleanup and make background coding tasks more reliable. ## Model selection for different tasks - The Agents panel now includes a model picker. - Users can choose faster models for routine work, stronger models for complex refactoring or integration tests, or let GitHub select automatically. - Model selection is currently available to Copilot Pro and Pro+ users; Business and Enterprise support is planned. ## Self-review before pull requests - Copilot coding agent now runs Copilot code review on its own changes before opening a pull request. - It incorporates feedback and improves the patch, such as simplifying overly complex code. - Users can inspect the review and iteration steps in the task logs before reviewing the resulting pull request. ## Integrated security checks - The agent performs code scanning, secret scanning, and dependency vulnerability checks during its workflow. - Vulnerable dependencies, exposed API keys, and other risky patterns can be identified before a pull request is created. - These code-scanning capabilities are provided without requiring a separate GitHub Advanced Security subscription for this workflow. ## Custom agents for team processes - Teams can define specialized agents in `.github/agents/`. - Custom agents can enforce repeatable procedures, such as benchmarking code before and after a performance change. - Agents can be shared across an organization or enterprise to standardize development practices. - The article describes a custom performance agent that achieved a 99% improvement on a targeted lookup function. ## Cloud and local CLI handoff - Cloud coding-agent sessions can be continued locally with their branch, logs, and context intact. - Users can select “Continue in Copilot CLI” and run the provided command in a terminal. - Pressing `&` in the CLI delegates work back to the cloud without restarting the task. GitHub recommends using these features to match models and workflows to each task, while reviewing the agent’s logs and pull requests. Planned capabilities include private mode, planning before coding, and tasks that produce summaries or reports instead of pull requests.

microsoft

Common annotated security keys (opens in new tab)

GitHub’s improved security-token format demonstrated that fixed signatures and checksums can sharply reduce both false positives and missed secret detections. Microsoft applies these ideas across its services and proposes the open-source Common Annotated Security Standard (CASK), a shared format for identifiable secrets. CASK is intended to make scanning faster, more accurate, and easier to apply across an entire ecosystem without disrupting developers. ## Identifiable Secrets and Better Detection - “Identifiable” keys combine: - A fixed signature that reliably identifies the format. - A checksum that validates whether a detected string is a real key. - These features reduce scanner noise and missed findings. - Microsoft can hard-block identifiable keys from being stored in source code, work items, and similar locations with high confidence. - Scanners can detect the common format first and classify the specific service provider later, if needed. ## The Common Annotated Security Standard - CASK defines platform-agnostic requirements for minted security keys. - It reserves space for individual platforms and providers to encode service-specific metadata. - Microsoft has defined Azure-specific metadata within this reserved area. - A shared standard lowers the effort required for security tools to protect multiple service providers. - Other providers can adopt the same core format. ## CASK Key Requirements ### Alphanumeric Encoding - Keys use only the BASE62 alphabet. - Avoiding special characters allows keys to be transmitted without escaping or additional encoding. ### Strong Entropy - Each key contains 52 randomized encoded characters. - This provides approximately 310 bits of entropy. - The design is intended to prevent brute-force attacks, including in a post-quantum environment. ### Fixed Signatures - Every CASK key includes: - The standard signature `JQQJ`. - A provider-specific signature. - Microsoft observed `JQQJ` to be rare in both open-source and internal code, enabling fast and accurate detection. - Azure DevOps uses `AZDO` as its provider signature. - These signatures allow tools to detect CASK keys generically while still supporting provider-specific classification. ### Metadata and Testing Support - Keys include their creation month and year. - Timestamps support incident response and key-rotation enforcement. - CASK reserves dedicated test keys so developers can test scanners and security controls without exposing real credentials. - Microsoft plans to provide more details about Azure-specific metadata. Microsoft recommends that service providers adopt CASK and contribute feedback as the standard evolves.