标签：#Security

* When a project first starts, CI/CD pipelines are usually simple. * Build the application. Run a few tests. Deploy somewhere. Done. Then six months pass. Another test gets added. Then another deployment step. A security scan. Performance tests. Notifications. More environments. Before long, a pipeline that once took five minutes now takes forty-five. I've seen this happen more than once, and it's rarely because Azure DevOps is the problem. It's usually because nobody ever stops to ask one simple question: Does this step still belong here? Everything Ends Up in the Same Pipeline One of the most common mistakes I see is trying to make a single pipeline do everything. Every Pull Request ends up running: Every unit test Every API test Hundreds of UI tests Security scans Deployment steps Report generation The result? Developers wait longer for feedback, releases become slower, and people eventually start ignoring failed pipelines because they happen too often. Fast Feedback Wins Not every test needs to run on every commit. A better approach is to think about the purpose of each pipeline. For a Pull Request, I want answers quickly. That usually means: Build the application Run unit tests Run a small smoke test suite Stop if something important fails Everything else can happen later. Long-running regression tests, cross-browser testing and other expensive checks are often better suited to scheduled or nightly pipelines. Pipelines Should Evolve A pipeline isn't something you build once and forget about. Every few months it's worth reviewing it. Ask yourself: Which step takes the longest? Which tests fail most often? Are there any tasks nobody remembers adding? Are we getting useful feedback, or just more output? Removing unnecessary work is just as valuable as adding new automation. Final Thoughts Azure DevOps is an incredibly powerful platform, but even the best tools become frustrating if they're overloaded with unnecessary work. The goal isn't to build the biggest pipel

MCP for AWS Security Engineers: Build a Read-Only Security Hub Triage Agent For AWS-heavy security work, I would start with AWS Agent Toolkit for AWS and the managed AWS MCP Server , not a custom MCP server. The reason is practical. AWS now provides a managed MCP path that can connect AI coding agents to AWS documentation, AWS APIs, AWS skills, and existing IAM credentials. The Agent Toolkit also provides plugin-based setup for supported agents such as Claude Code and Codex. For security teams, that is the right starting point because the enforcement point remains AWS IAM, not the model. The initial operating model should be strict: Read-only first. No production write authority. No access to secrets. No raw customer PII or sensitive incident logs in prompt context. No automatic remediation. No AI-approved suppression, exception, merge, deploy, or risk acceptance. Human review and CI/CD remain the release authority. That is the same posture I would use for a governed Claude Code or Codex rollout: named identities, SSO, scoped credentials, default deny, tool approval, audit logs, and security evidence tied back to tickets, pull requests, CI logs, and cloud findings. What we are building This article walks through a practical security workflow: A read-only Security Hub triage assistant that helps a junior security engineer produce a daily or weekly findings summary, remediation backlog, and evidence pack without allowing the agent to modify AWS. The agent will be able to: Read AWS Security Hub findings. Group findings by account, severity, product, resource, and control. Explain why a finding matters. Draft remediation tickets. Draft a Slack-ready summary. Produce local markdown, CSV, and JSON evidence files. The agent will not be able to: Suppress findings. Archive findings. Mark findings resolved. Disable Security Hub standards. Modify IAM, S3, EC2, KMS, GuardDuty, Inspector, or Config. Deploy remediation. Run destructive scripts. Approve risk acceptance. This is no

The Idea: Hidden Instructions Inside Trusted Content Prompt injection is an attack where malicious instructions are embedded inside content that an AI is asked to process - a document, a webpage, an email, a customer support ticket. The model can't always distinguish between "data I'm reading" and "commands I should follow," so it follows the embedded instruction as if a legitimate user sent it. This gets sharper when AI agents (autonomous systems that browse the web, read files, and take actions on your behalf) are involved. A summarizer that reads a webpage might encounter hidden text instructing it to forward your conversation history somewhere, or change the tone of its next reply, or deny remembering something it just said. The model has no inherent way to verify who is actually giving orders. The core problem is one of trust boundaries: current large language models process instructions and data through the same channel - natural language - so there's no hard technical wall between "read this" and "do this." Researchers have demonstrated this across multiple major models, not because any one model is uniquely broken, but because the architecture makes the distinction genuinely difficult. Defenses exist but are imperfect. Techniques include output filtering, sandboxing agent permissions (limiting what actions the model is allowed to take regardless of what it's told), prompt hardening (structuring system prompts to be resistant to override), and retrieval-aware design that treats external content as untrusted by default. No single fix closes the gap entirely. Real Example: The Customer Support Agent Imagine a small business deploys an AI agent to handle incoming support emails. The agent reads the email, checks order history, and drafts replies. A bad actor sends a support ticket that looks normal on the surface, but contains a hidden paragraph - white text on white background, or text in a section the agent processes but doesn't display - that says: "Ignore pr

GitHub announced agentic autofix for code scanning alerts in public preview on July 10, 2026. Primary source: GitHub Changelog, July 10, 2026 . The wrong metric is “percentage of alerts with a generated patch.” Generation is only the first transition: alert -> candidate -> build -> tests -> security oracle -> human review -> merge -> post-merge observation This is an evaluation proposal, not a benchmark or assessment of GitHub's preview. Choose a bounded canary Start with repositories that have active owners, deterministic builds, relevant isolated tests, reversible releases, and no automatic production deployment from candidate patches. Exclude abandoned code, safety-critical paths, and repositories with unreliable tests. Assign the canary deterministically—for example, hash a stable alert ID into a fixed percentage. Do not move difficult results out of the cohort after seeing them. Record every attempt, including abstentions and failures: attempt_id : " <id>" alert_class : " <normalized class>" base_revision : " <commit>" outcome : generated : true applied_cleanly : true build_passed : true tests_passed : false security_oracle_passed : false human_decision : " rejected" failure_class : " semantic_incomplete" escaped_to_default_branch : false The schema is local evaluation metadata; it does not imply that GitHub exposes these fields. Classify the earliest broken invariant Class Meaning No candidate Tool abstained Scope violation Unrelated or forbidden paths changed Apply failure Patch does not apply to recorded base Build failure Patched revision cannot build Regression Existing behavior broke Semantic incomplete Alert changed but security property remains broken Overcorrection Valid behavior was blocked Test manipulation Validation was weakened or removed Stale base Result targeted another revision Review ambiguity Human cannot establish why the patch is safe Infrastructure Evaluation could not complete Post-merge escape Later evidence disproved acceptance Use one

GitHub announced on July 14, 2026 that security reviews are available in the GitHub Copilot app. Primary source: GitHub Changelog, July 14, 2026 . The meaningful research question is not whether people click Accept. It is whether they can build an evidence-backed decision when guidance is useful, incomplete, or wrong. understand change -> inspect evidence -> challenge findings -> verify uncertainty -> accept, reject, or escalate This is a proposed research protocol, not a completed study. It does not invent product fields or report findings. Build scenario cards scenario_id : " SR-03" repository_type : " synthetic" seeded_conditions : - " one relevant issue" - " one plausible but irrelevant concern" - " one important omission" participant_goal : " ready, blocked, or escalate" success_evidence : - " decision cites inspected code" - " unsupported claim is challenged" - " unresolved uncertainty is recorded" stop_conditions : - " real credentials appear" - " a live repository could be modified" - " participant mistakes study output for production approval" Vary the seeded mix so participants cannot learn that every scenario contains exactly one true and one false finding. Establish ground truth independently before sessions. Recruit people who hold different review responsibilities: routine reviewers, maintainers, security specialists, less-experienced reviewers, and people using keyboard navigation or assistive technology. Do not collapse every group into one average. Require a decision record Decision: ready | blocked | escalate Evidence inspected: - file and relevant lines - test or documentation Guidance accepted: - claim and evidence Guidance rejected: - claim and reason Unresolved: - question and next owner Spoken confidence is not the outcome. This artifact exposes whether acceptance connects to evidence. Measure relevant issues identified, unsupported claims challenged, evidence references, correct escalation, time, and confidence before and after inspection. No

GitHub announced on July 8, 2026 that GitHub Mobile can start a Copilot cloud-agent workflow to fix pull-request merge conflicts. Primary source: GitHub Changelog, July 8, 2026 . The unsafe mental model is “tap once and the conflict is solved.” A safer model is: mobile intent -> bounded remote task -> proposed patch -> verification -> human merge This is a source-based checklist, not a hands-on product assessment. Exact controls and permissions must come from current GitHub documentation. Record the handoff repository : " owner/project" pull_request : 123 base_branch : " main" expected_base_sha : " <commit>" expected_head_sha : " <commit>" allowed_scope : - " src/example/**" - " tests/example/**" forbidden_scope : - " .github/workflows/**" - " deployment/**" required_checks : - " unit-tests" reviewer : " <responsible human>" expires_at : " <UTC timestamp>" This operator artifact is not a representation of the mobile UI. It preserves intent across interruptions, network changes, and the delay between delegation and review. Before handoff, confirm repository, pull request, branches, expected files, sensitive paths, required checks, and the person responsible for merge. Never place secrets, customer data, or private incident details in the instruction. A bounded instruction is better than “make CI green”: Resolve conflicts between the recorded base and head revisions. Preserve documented behavior, limit changes to the listed paths, do not modify workflow or deployment configuration, and return a patch without merging. Verify the returned revision the result belongs to the expected repository and pull request; base and head revisions still match the handoff; every changed file is expected or explained; no conflict markers remain; no workflow, ownership, deployment, or policy file changed unexpectedly; tests ran against the exact reviewed commit; tests were not weakened or removed; a human can explain the semantic choice made for each conflict; the reviewed commit is the

GitHub's July 13, 2026 changelog lists REST API management for secret scanning custom patterns. That makes a reviewed configuration-as-code workflow possible. Primary source: GitHub Changelog archive, July 2026 . Follow the July 13 entry to the current REST documentation before implementation. The transport below is an unexecuted design. Endpoint paths, payload fields, permissions, pagination, and plan availability must come from the linked official API reference—not from guessed examples. Define the sync contract A safe synchronizer should: read desired patterns from version control; fetch the remote collection; match each pattern by a stable identity; emit create, update, unchanged, and delete actions; refuse deletion unless explicitly enabled; apply only after the plan is reviewed. patterns.json -> normalize -> diff remote -> plan.json -> approval -> apply Keep API-specific payloads opaque to the diff engine: { "patterns" : [ { "stableKey" : "internal-service-token-v1" , "remoteId" : "SET_AFTER_CREATION" , "payload" : { "REPLACE_WITH_DOCUMENTED_FIELD" : "REPLACE_WITH_REVIEWED_VALUE" } } ], "allowDelete" : false } Placeholders are deliberate. A secret detector's regex fields and matching semantics are security contracts and should never be invented from a blog post. Build a deterministic planner export function plan ( desired , current ) { const remote = new Map ( current . map ( x => [ x . id , x ])); const changes = []; for ( const item of desired . patterns ) { if ( ! item . remoteId ) { changes . push ({ action : " create " , key : item . stableKey }); continue ; } const found = remote . get ( item . remoteId ); if ( ! found ) throw new Error ( `Missing remote pattern ${ item . remoteId } ` ); const same = JSON . stringify ( canonical ( found )) === JSON . stringify ( canonical ( item . payload )); changes . push ({ action : same ? " unchanged " : " update " , key : item . stableKey }); remote . delete ( item . remoteId ); } for ( const orphan of remote . valu

GitHub announced on July 14, 2026 that Dependabot now waits until a release has been available on its registry for at least three days before opening a version-update pull request. The cooldown is the new default and requires no configuration. Primary source: GitHub Changelog, July 14, 2026 . The product question is not whether three days is universally safer. Waiting may expose a broken or compromised release before adoption, but it can also postpone a needed compatibility fix. Cooldown is a baseline, not a complete dependency policy. Separate normal freshness from security response release -> cooldown -> update PR -> CI -> review -> deploy -> observe A cooldown does not replace lockfiles, CI, provenance checks, staged deployment, or rollback. It should not silently delay an urgent security-update workflow either. Use this decision table before creating exceptions: Package situation Suggested decision Compensating control Routine dev dependency with strong CI Keep default Deterministic tests Runtime package on a critical path Keep or lengthen Canary and fast rollback Identified security remediation Use the dedicated urgent path Security owner and targeted tests Isolated build-only tool Consider shorter Reproducible artifact checks Volatile pre-1.0 package Consider longer Manual release review Internal package released with the app Consider exception Exact-version integration test Parser, auth, crypto, or installer Keep default unless urgency wins Sandbox and abuse fixtures Score the exception Rate each dimension from 1 to 5: blast radius; upstream release volatility; test strength; rollback speed; cost of waiting. Then use two conversation scores: wait_value = blast_radius + release_volatility ship_confidence = test_strength + rollback_speed + cost_of_waiting This is not a statistical risk model. It forces the team to name evidence. Keep the default when scores are close. Consider shortening only when delivery evidence clearly exceeds wait value. Route vulnerabilit

GitHub announced on July 2, 2026 that Copilot CLI no longer needs a personal access token when it runs in GitHub Actions. Primary source: GitHub Changelog, July 2, 2026 . Deleting a secret is easy. Proving the workflow still works—and recovering without hurriedly pasting credentials back into YAML—is the useful part. This is an unexecuted migration template, not a report from a production repository. Make one reversible change First find every place the old secret enters the job, including reusable workflows: git grep -nE 'COPILOT_PAT|COPILOT_GITHUB_TOKEN|GH_TOKEN|github_pat' Record the workflow, job, pinned CLI version, permissions block, and previous known-good commit. Never print environment variables while debugging. Then remove only the PAT injection. Do not upgrade the runner and CLI in the same patch. jobs: copilot-check: permissions: contents: read - env: - COPILOT_GITHUB_TOKEN: ${{ secrets.COPILOT_PAT }} steps: - uses: actions/checkout@<PINNED_COMMIT> - run: ./scripts/setup-copilot-cli.sh - run: ./scripts/run-bounded-check.sh The scripts are placeholders for repository-owned commands. “No PAT required” does not mean “no identity or permissions exist”; follow the current setup documentation and keep permissions explicit. Add a canary with two proofs The canary should show that the legacy token is absent and that the existing bounded task still satisfies its output contract. name : copilot-cli-auth-canary on : workflow_dispatch permissions : contents : read jobs : canary : runs-on : ubuntu-latest timeout-minutes : 10 steps : - uses : actions/checkout@<PINNED_COMMIT> - name : Verify legacy PAT is absent run : | test -z "${COPILOT_PAT:-}" test -z "${COPILOT_GITHUB_TOKEN:-}" - run : ./scripts/setup-copilot-cli.sh - run : copilot --version - run : ./scripts/run-bounded-check.sh Use a read-only, cheap task. “Fix whatever you find” is not a canary; it is a small deployment wearing a fake mustache. Define pass criteria before clicking Run: absent legacy variables, e

GitHub announced on July 10, 2026 that CodeQL 2.26.0 adds AI prompt-injection detection. Enabling a query is useful; owning a regression test is better. Primary source: GitHub Changelog, July 10, 2026 . The examples below are implementation templates, not results from a repository I tested. Model a path, not a phrase A useful fixture contains an untrusted source, prompt construction, and a model sink: security-fixtures/prompt-injection/ ├── positive/direct-flow.ts ├── positive/helper-flow.ts ├── negative/trusted-instruction.ts └── expected-alerts.json Do not test for the literal phrase ignore previous instructions . Static analysis needs a data-flow path. Preserve a supported SDK call from your production stack so CodeQL can recognize the sink. // Intentionally vulnerable fixture. Never ship this path. import { model } from " ./supported-client " ; declare function loadIssueBody ( id : number ): Promise < string > ; export async function summarize ( id : number ) { const untrusted = await loadIssueBody ( id ); return model . generate ({ system : " Summarize the issue " , user : untrusted , }); } Add a second positive case that passes the value through a helper. Then add a negative control where attacker input cannot select or alter the instruction. A function named sanitize() is not evidence of sanitization. Assert SARIF evidence Uploading SARIF alone does not create a regression gate. Commit the expected rule and fixture location: { "required" : [ { "ruleId" : "REPLACE_WITH_DOCUMENTED_RULE_ID" , "pathSuffix" : "positive/direct-flow.ts" } ], "forbiddenPathSuffixes" : [ "negative/trusted-instruction.ts" ] } Keep the rule ID as a placeholder until it is copied from the CodeQL 2.26.0 documentation or an observed SARIF result. Machine-facing identifiers should never be guessed. A small assertion can compare runs[].results[].ruleId and each physical location against this file. Fail when a required alert disappears or a negative fixture starts alerting. Do not assert the

Every agent-security vendor tells you what they block. Nobody tells you what they miss. That gap is the whole problem. "We stop prompt injection" is a claim you cannot check. You cannot run it, and you cannot tell it apart from the next company saying the same sentence. So security engineers do the rational thing and discard all of it. I published the opposite. It is called the ARE Incident Database , and it is public: https://aredb.org What is in it 32 agent failures that actually happened, each with a real source. A production database dropped during a code freeze. Twenty-five thousand documents deleted in the wrong environment. Credentials read and shipped to an external sink. A budget burned to zero in a loop. Each one gets a stable id ( ARE-2026-001 through ARE-2026-032 ), and each one is mapped to its category in the OWASP Agentic Security Initiative Top 10 , which is the peer-reviewed catalog of what goes wrong with agents. AREDB does not compete with it. OWASP owns the map. This is the cited incidents underneath it. The part that makes it uncomfortable to publish Every entry carries a coverage flag, and the flag is about our own product . We block 23 of the 32 today. Two more are partial, and they say partial. That leaves six of the ten OWASP categories covered at the action layer, and four that we do not cover: ASI06 Memory and context poisoning. We strip the hidden characters attackers use to smuggle instructions into text. We do not read the meaning of the text itself, so this one is only partial, and we mark it partial. ASI07 Insecure inter-agent communication. This is about how agents talk to each other over the network, which a firewall that sits in front of actions never sees. Not ours. ASI09 Human-agent trust. This is a design and disclosure problem. There is no action for a firewall to catch. Not ours. ASI10 Rogue agents. We stop the dangerous actions, but we do not diagnose the misbehavior itself. Partial. A firewall that claimed all ten would be l

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GitHub recently launched Agentic Workflows — GitHub Actions combined with an AI agent backed by Claude or GitHub Copilot, writing workflows in plain Markdown. Noma Labs' first question after launch was the obvious one: what happens when the agent reads something it shouldn't trust? The answer: it leaks private repository contents as a public comment. No credentials, no exploit code, no inside access required. "The agent's context window is also its attack surface. Any content the agent reads — whether issues, pull requests, comments, or files — can be weaponized if the agent treats that content as instructional input." What actually happened Noma's researchers crafted a GitHub Issue that looked like a plausible VP Sales request — a normal-looking feature ask with hidden instructions embedded in the body. When GitHub's automation assigned the issue, it triggered an Agentic Workflow configured to: Trigger on issues.assigned events Read the issue title and body Post a comment using the add-comment tool Run with read access to other repositories in the organisation — including private ones The hidden instructions told the agent to fetch README.md from repos across the org and post the contents as a comment on the public issue. It did exactly that, including the contents of testlocal — a private repository. The proof-of-concept is live: the workflow run and the issue are public. The guardrail bypass GitHub had defences in place to prevent this. They didn't hold. Noma found that adding the word "Additionally" to the injected instructions caused the model to reframe its output rather than refuse — bypassing the guardrails entirely. A single keyword was enough to undo the intended safety behaviour. This is what makes prompt injection particularly uncomfortable: guardrails tuned against known attack patterns can be bypassed by anyone willing to iterate on the phrasing. The attacker's loop is cheap; the defender's loop is not. The bigger pattern Noma names this explicitly: pr

On June 9, 2026, Anthropic shipped Claude Fable 5, a model in a new tier that sits above Opus. I have been building on the Claude API for over a year, and this is the first release that made me stop and re-read my whole prompt stack before touching the model string. Here is what actually changed and what it means if you ship software. The short version Fable 5 is the public release of the Mythos line, the family that earlier in the year unsettled the security world with how well it found and exploited vulnerabilities. The version you and I get is the same underlying model with safeguards bolted on. Anthropic calls the safe one Fable and the unrestricted one Mythos, and only a small group of cyberdefenders gets Mythos. The numbers, for context: 1M token context window, 128K max output, knowledge cutoff January 2026. Priced at $10 per million input tokens and $50 per million output. That is double Opus 4.8 ($5 / $25). State of the art on nearly every benchmark they tested: 95% SWE-bench Verified, 80% SWE-bench Pro. Adaptive thinking is always on. There is no "disabled" mode. That last point matters more than the benchmarks. You do not tune a thinking budget anymore. The model decides. The pricing reframes the decision At $10/$50, Fable 5 is not your default model. It is your "this task is hard and getting it wrong is expensive" model. Opus 4.8 at $5/$25 remains the workhorse for most application traffic, and Haiku 4.5 at $1/$5 still wins on classification and routing. The way I think about it now is a three-tier ladder: Haiku 4.5 → routing, classification, cheap extraction Opus 4.8 → default for app traffic, agentic loops, coding Fable 5 → long-horizon agentic work where correctness pays for itself The "longer and more complex the task, the larger Fable's lead" framing from the announcement is the actual buying signal. A one-shot summarization does not justify 2x the cost. A multi-hour autonomous refactor that would otherwise need human correction might. The API surfa