おなじみのプロセスやワークフローを特徴とする継続的インテグレーション（CI）の時代は、急速に終わりを迎えつつあります。従来のCIは、開発者が小規模で頻繁な反復的なコミットを行うことを前提としていました。今日、「継続的」という側面そのものが変化しつつあります。

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\"WebPage\",\n \"@id\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/\",\n \"url\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/\",\n \"name\": \"The future is AC/DC: the Agent Centric Development Cycle | Sonar\",\n \"description\": \"The era of Continuous Integration, with its familiar processes and workflows, is rapidly coming to an end. Traditional CI relies on developers making small, frequent, iterative commits. Today, the “continuous” part is changing.\",\n \"inLanguage\": \"en\",\n \"publisher\": {\n \"@id\": \"https://www.sonarsource.com/#organization\"\n },\n \"sameAs\": [\n \"https://en.wikipedia.org/wiki/SonarQube\",\n \"https://en.wikipedia.org/wiki/Sonar_(company)\",\n \"https://en.wikipedia.org/wiki/Artificial_intelligence\",\n \"https://en.wikipedia.org/wiki/Systems_development_life_cycle\",\n \"https://en.wikipedia.org/wiki/Governance\",\n \"https://en.wikipedia.org/wiki/Software_development\",\n \"https://en.wikipedia.org/wiki/Agent-oriented_software_engineering\",\n \"https://en.wikipedia.org/wiki/AI_agent\"\n ]\n },\n {\n \"@type\": \"BlogPosting\",\n \"@id\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/#article\",\n \"mainEntityOfPage\": {\n \"@id\": \"https://www.sonarsource.com/blog/the-future-is-ac-dc-the-agent-centric-development-cycle/\"\n },\n \"headline\": \"The future is AC/DC: the Agent Centric Development Cycle\",\n \"description\": \"Continuous Integration is changing as agents work asynchronously and deliver larger code payloads. Sonar calls the emerging paradigm the Agent Centric Development Cycle (AC/DC).\",\n \"abstract\": \"The Agent Centric Development Cycle (AC/DC) describes Sonar's vision for the next evolution of the software development lifecycle, in which AI agents autonomously write, scan, and fix code within a continuous loop governed by SonarQube verification. In this model, AI coding agents such as Claude Code or Cursor use the SonarQube MCP Server to query quality gate status and issue data, self-correcting their output before a human developer reviews a pull request. AC/DC reframes the developer's role from primary code author to agent supervisor and quality governor, setting the standards that automated agents must meet rather than writing every line of code manually. Sonar's MCP Server, Agentic Analysis beta, and AI Code Assurance capability provide the tooling infrastructure needed to implement AC/DC workflows at scale within enterprise development environments.\",\n \"image\": [\n \"https://assets-eu-01.kc-usercontent.com/ef593040-b591-0198-9506-ed88b30bc023/7ec302f1-ae36-4b20-9bf1-1bbacb17cdd8/ac_dc_blog_hero_social_landscape_2x.webp?auto=format&fit=crop&h=1256&w=2400\"\n ],\n \"datePublished\": \"2026-03-02\",\n \"dateModified\": \"2026-03-02\",\n \"timeRequired\": \"PT14M\",\n \"articleSection\": \"AI\",\n \"inLanguage\": \"en\",\n \"author\": {\n \"@type\": \"Person\",\n \"name\": \"Tariq Shaukat\",\n \"jobTitle\": \"CEO\",\n \"image\": \"https://assets-eu-01.kc-usercontent.com/ef593040-b591-0198-9506-ed88b30bc023/82e25ff2-5d3d-479a-8459-f9547274b2b1/698%20-%20Tariq%20Shaukat%402x.jpg?auto=format&fit=crop&h=800&w=800\",\n \"worksFor\": {\n \"@id\": \"https://www.sonarsource.com/#organization\"\n }\n },\n \"publisher\": {\n \"@id\": \"https://www.sonarsource.com/#organization\"\n }\n }\n ]\n}",{"_15":329,"_39":54,"_41":7424},[],{"_15":333,"_39":40,"_41":46},{"_15":336,"_39":40,"_41":46},{"_15":339,"_39":85,"_41":7428},[],{"_15":343,"_39":40,"_41":46},{"_15":346,"_39":40,"_41":46},{"_15":349,"_39":85,"_41":7432},[],{"_15":353,"_39":67,"_41":7434,"_73":7435},[],[],{"_187":7272,"_244":245,"_246":7437,"_248":249,"_250":7438,"_15":7439,"_253":7440,"_39":7441,"_256":257,"_258":245},"17f3ca8a-a85d-4fb8-97fc-2782ffe2e42a","2026-04-08T17:50:26.8601498Z","BlogPost | The future is AC/DC",[],"article_page",{"_15":374,"_39":67,"_41":7443,"_73":7444},[],[],{"_187":7266,"_244":245,"_246":7446,"_248":249,"_250":7447,"_15":7448,"_253":7449,"_39":382,"_256":257,"_258":245},"6ea89a7c-8769-4502-8de7-e2ebb46a40a8","2026-03-02T22:34:54.2797306Z","The future is AC/DC | Link action",[],{"_15":385,"_39":67,"_41":7451,"_73":7452},[],[],{"_187":7251,"_244":245,"_246":7454,"_248":367,"_250":7455,"_15":7456,"_253":7457,"_39":393,"_256":257,"_258":245},"3f86df8c-ba6a-4590-b52b-6409b4bc633a","2026-04-30T13:17:30.2399051Z","The future is AC/DC | Link",[],{"_15":148,"_39":54,"_41":7459},[7460],{"_15":7461,"_187":7462},"Text Left","left","padding__top",{"_15":7465,"_39":54,"_41":7466},"Padding - Top",[],"padding__bottom",{"_15":7469,"_39":54,"_41":7470},"Padding - Bottom",[],{"_15":7183,"_39":67,"_41":7472,"_73":7473},[],[],{"_15":4034,"_39":67,"_41":7475,"_73":7476},[],[],{"_187":7220,"_244":245,"_246":7478,"_248":367,"_250":7479,"_15":7480,"_253":7481,"_39":7482,"_256":257,"_258":245},"fa5786e2-58b2-4dba-9df3-27d44b9a183c","2026-04-30T13:17:15.3508509Z","Featured Post - Card block",[],"n5050_card_block",{"_15":7183,"_39":67,"_41":7484,"_73":7485},[],[],{"_15":4034,"_39":67,"_41":7487,"_73":7488},[],[],{"_187":70,"_244":245,"_246":7490,"_248":367,"_250":7491,"_15":7492,"_253":7493,"_39":7494,"_256":257,"_258":245},"b8ebeb63-fdad-4fd6-b337-a452480c087a","2026-04-30T13:16:43.3410253Z","Blog | Hero Block Index",[],"hero_block_index",{"_34":7496,"_242":7646},{"_7312":7497,"_7358":7506,"_7510":7511,"_120":7515,"_7517":7518,"_7625":7626,"_7632":7633,"_7639":7640},{"_15":7314,"_39":67,"_41":7498,"_73":7499},[7316],[7500],{"_34":7501,"_242":7504},{"_15":7502,"_47":7503},{"_15":7290,"_39":40,"_41":42},{"_15":49,"_39":40,"_41":50},{"_187":7316,"_244":245,"_246":7323,"_248":249,"_250":7324,"_15":42,"_253":7505,"_39":7326,"_256":257,"_258":245},[],{"_15":7507,"_39":67,"_41":7508,"_73":7509},"Article Tag",[],[],"exclude_facets_from_filters",{"_15":7512,"_39":67,"_41":7513,"_73":7514},"Exclude facets from filters",[],[],{"_15":122,"_39":54,"_41":7516},[],"cta_card",{"_15":7519,"_39":67,"_41":7520,"_73":7522},"CTA Card",[7521],"newsletter_signup___cta_card",[7523],{"_34":7524,"_242":7619},{"_7525":7526},"content",{"_15":7527,"_39":67,"_41":7528,"_73":7530},"Content",[7529],"mid_page_form_container___blog_signup___card___con",[7531],{"_34":7532,"_242":7613},{"_7212":7533,"_7535":7536,"_7230":7539,"_7546":7547,"_150":7554,"_7556":7557,"_120":7565,"_7567":7568,"_7181":7607,"_7186":7610},{"_15":7228,"_39":40,"_41":7534},"Get new blog posts delivered directly to your inbox!","success_headline",{"_15":7537,"_39":40,"_41":7538},"Success Headline","You’re all set!",{"_7232":7540,"_7234":7541,"_73":7542,"_398":7543,"_15":7544,"_39":7239,"_41":7545},[],[],[],[],"Body","

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March 30, 2026 — Early Access terms updated to reflect that Remediation Agent is now in beta.

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code execution and Claude Code CLI: How Claude executed code before you click 'trust'","We discovered different ways an untrusted folder can execute arbitrary code in Claude Code before the user is prompted with the trust dialog, allowing for potential compromise when cloning untrusted projects!","Yaniv Nizry","cta","creationDate","2026-04-30T16:00:00Z",[12734],"Vulnerability research","keywords",[12737],"Code Security",[12739],"Category:Vulnerability research","order","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 13 - 4pm CEST | 9am CDT Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Anthropic’s Claude Code CLI has become an increasingly popular tool for developers, driving over 10 million weekly downloads on NPM (@anthropic-ai/claude-code). The introduction of Model Context Protocol (MCP) gives the AI agent sensitive, extensive capabilities, significantly raising the security stakes of this popular tool. Anthropic has been proactive in implementing defenses to tackle these risks, such as running the agent with strict read-only permissions by default and implementing a \"trust\" gate for new projects. However, while much of the security discussion focuses on new LLM risks like prompt injection, the old security flaws, such as trusting config files, can still apply.\n\nIn this blog post, we detail two critical issues we identified that, before being patched, would have allowed an attacker to bypass Claude Code’s primary security defense: the trust dialog. This means that, in affected versions, simply cloning or downloading an untrusted repository and running the tool inside it would be enough to compromise a developer’s environment. As of December 16, 2025, Anthropic has patched the vulnerabilities described below.\n\nImpact\n\nWhen a victim ran Claude Code inside a malicious, untrusted project folder, an attacker was able to execute arbitrary code on the victim's system, bypassing the trust dialog. This could have led to a full compromise of the developer's machine and environment.\n\nAnthropic patched Claude Code to fix the following issues in v2.0.71, so we recommend updating to the latest version:\n\nArbitrary code execution via git project config\n\nArbitrary code execution via Claude project settings\n\nHere's a mock demo video of the attack prior to the patch being made:\n\nTechnical details\n\nWhile developers sharing code is a common habit, it also poses a significant security risk. What if the person who shared the code with you has malicious intentions? To tackle this, Anthropic’s security model follows a similar approach to other coding platforms, such as VSCode, by prompting the user with a trust dialog before accessing the tool. This way, developers explicitly acknowledge the risk of running Claude Code in an untrusted workspace before the tool has broader access. \n\nArbitrary code execution via git project config\n\nWhen we started researching Claude Code, we focused on the pre-trust initialization phase. Take a look at these logs that follow file access and command executions of Claude before the trust dialog, and see if you can capture what raised our concerns:\n\nIf you follow our blogs closely, we covered a very similar issue in the past. The simple and innocent-looking git status command is exactly what enabled attackers to bypass the trust dialog in Microsoft Visual Studio Code < 1.63.1 (CVE-2021-43891), and JetBrains IDEs < 2021.3.1 (CVE-2022-24346).\n\nThis behavior can be exploited because Git supports a core.fsmonitor configuration option in its local .git/config file. This option is designed to be used as a command that will identify all files that may have changed since the requested date/time (source). But if a malicious project sets this value to an arbitrary command, Git will execute it when git status is run, which happens before Claude Code's security prompt.\n\nThe attacker would simply add the following configuration to the malicious shared project:\n\nAnd running the claude command within this folder will execute the fsmonitor before the trust dialog is approved:\n\nArbitrary code execution via git project config, round 2\n\nIn version 2.0.34, Claude was updated in a way that mitigated the specific vulnerability by no longer running git status before the user approved the trust dialog. However, a related issue persisted. In the then-latest version (2.0.50), we found that Claude was still executing several other git commands without user approval:\n\ngit remote get-url origin\n\ngit config get user.email\n\ngit rev-parse --is-inside-work-tree\n\ngit log -n 1000 --pretty=format: --name-only --diff-filter=M\n\ngit worktree list\n\nAnd since the git configuration and ecosystem (commands, attributes, hooks, etc) is huge, we knew there could be a new attacker vector here that allows attackers to execute arbitrary code when running one of the commands above in an untrusted folder. We initially looked at core.pager as it provides a command that will run when there are pagers (like less or more) in the terminal, this works on git log; however, when we ran it with Claude, it didn’t. This is because Claude is executed from Node.js, so exec function captures the entire output (stdout) of the child process as a string in memory and then passes it to the callback, so there is no TTY (meaning no interactive terminal), and therefore no pager like less or more will be launched.\n\nAnother idea was because the git log command uses the flag --diff-filter=M it should run git diff (diff-filter) and with this, there are configs such as diff.external or filters with .gitattributes that should provide a straightforward arbitrary code execution. However, this didn’t work as well because git log needs to explicitly allow the extensions via the --ext-diff flag, and show the file content in the log, but Claude runs the command with --name-only.\n\nSo, it was clear that this would not be a single, straightforward configuration that will be executed as a command. After a bit of searching, we stumbled upon log.showSignature, which basically adds the --show-signature argument to git log. The show-signature argument is meant to verify signed commit objects by passing the signature to gpg --verify. Meaning that the gpg command will also run, and now an attacker can take advantage of the gpg.program configuration, which specifies a pathname of the program to run instead of \"gpg\". First, for gpg --verify to run, the attacker would need a git project with a “signed” commit, so a new empty project won't work, but it is not hard to overcome with an existing sample project:\n\nAnd running the claude command within this folder will execute calc.sh before the trust dialog:\n\nArbitrary code execution via Claude project settings\n\nThe second vulnerability stems from another intended logic that is performed before the trust dialog and not after. This one is less subtle as it exploits Claude Code’s own local project settings from .claude/settings.json upon startup. Some of these settings are designed to execute code, and because local settings take precedence over the global ones, a malicious project can include a .claude/settings.json file to trigger arbitrary code execution before the trust dialog is presented.\n\nTwo settings were found to allow this:\n\napiKeyHelper: This setting is defined as a “Custom script, to be executed in /bin/sh” and is called using child_process.spawnSync upon startup.\n\nHooks: designed to execute commands upo","source","sonarsource.com","objectID","blogpost___arbitrary_code_execution_and_claude_cod-0","_snippetResult",{"_40":12748},{"_41":12749,"_12750":12751},"Products SonarQube products Cloud-based static analysis tool for your …","matchLevel","none","_highlightResult",{"_12723":12754,"_40":12771},{"_36":12755,"_89":12758,"_7281":12760,"_7312":12762,"_132":12764,"_7212":12766,"_12735":12768},{"_41":12727,"_12750":12751,"_12756":12757},"matchedWords",[],{"_41":12728,"_12750":12751,"_12756":12759},[],{"_41":12729,"_12750":12751,"_12756":12761},[],{"_41":42,"_12750":12751,"_12756":12763},[],{"_41":7381,"_12750":12751,"_12756":12765},[],{"_41":12727,"_12750":12751,"_12756":12767},[],[12769],{"_41":12737,"_12750":12751,"_12756":12770},[],{"_41":12741,"_12750":12751,"_12756":12772},[],{"_187":12063,"_250":12774,"_12723":12775,"_12683":12785,"_237":12712,"_12740":12712,"_40":12789,"_12742":12743,"_12744":12790,"_12746":12791,"_12752":12793},"2026-04-29T20:30:16.3966476Z",{"_39":12725,"_394":12776,"_36":12777,"_89":12778,"_7281":12779,"_7312":42,"_132":7381,"_7212":12777,"_12730":7392,"_12731":12780,"_7337":7314,"_7327":12781,"_12735":12784},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/a65d42f1-4ebe-415c-8b10-c91e5fb9ffa8/llm-concurrency-blog-index-2x.webp","GPT-5.5’s biggest blind spot: the Java bugs your tests won’t catch","Sonar’s LLM Leaderboard reveals concurrency bugs in AI generated Java code that pass tests but break in production due to thread timing issues.","Killian Carlsen-Phelan","2026-04-28T15:00:00Z",[2416,12782,12783],"Code security","Languages & frameworks",[],[12786,12787,12788],"Category:AI","Category:Code security","Category:Languages & frameworks","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 13 - 4pm CEST | 9am CDT Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. TL;DR overview\n\nConcurrency bugs are among the hardest defects to catch in AI-generated Java code because they pass functional tests but fail under production thread timing.\n\nSonar’s LLM Leaderboard analysis shows concurrency bug density varies 7x across models, with GPT-5.5 producing 170 bugs per million lines of code.\n\nCommon failure patterns include broken double-checked locking, unsound synchronization on value-based classes like Boolean, and holding locks during Thread.sleep() calls.\n\nStatic analysis identifies these thread-safety risks by analyzing code structurally, catching defects that standard test frameworks cannot reliably trigger.\n\nSonar's LLM Leaderboard evaluations have analyzed millions of lines of AI-generated Java code across multiple models. Concurrency bugs show up in every model's output, but at rates that vary more than almost any other bug category.\n\nWhat doesn't vary is the failure mode. These bugs compile and pass functional tests but break in production because their correctness depends on thread timing that no test framework controls. The patterns behind them are well-documented and detectable through static code analysis, but they live in the gap between code that passes tests and code that is thread-safe.\n\nHow concurrency rates vary across models\n\nSonar's evaluation framework runs each model through thousands of Java coding tasks (4,444 for the GPT-5.5 evaluation), executing multiple independent runs and analyzing the output with SonarQube's Java coding rules. The table below shows concurrency bug density for a sample of evaluated models.\n\nThe absolute rates span a 7x range across these models alone, and the leaderboard includes additional models that widen the picture further. Concurrency accounts for nearly 50% of all bugs in some model configurations and under 3% in others, so while some models produce concurrency as their dominant bug category by a wide margin, others are led by exception handling or type safety instead. A double-checked locking violation or a lock held during sleep behaves the same way in production regardless of which model generated it.\n\nThree patterns to watch for\n\nThe concurrency bugs that surface in these evaluations share a trait regardless of rate: their correctness depends on execution ordering and runtime object identity, not on what's written in the method body. A resource leak is visible in the code itself because you can point to the missing close() call. Whether or not double-checked locking is safe depends on the Java Memory Model's happens-before guarantees, and whether a synchronized block actually provides mutual exclusion depends on which object you're locking on and whether the JVM might be sharing that object with unrelated code. These are properties of how the program runs, not how it reads, and they're the reason concurrency bugs survive functional testing: a test exercises one execution ordering, and the bug lives in a different one.\n\nThe three patterns below, drawn from SonarQube's Java concurrency rules, each represent a different failure mode, specifically, a broken initialization sequence, a wrong lock object, and a lock held during sleep.\n\nDouble-checked locking (S2168)\n\nDouble-checked locking is meant to avoid synchronizing every call to a singleton accessor by checking null before and after the synchronized block:\n\nThe \"Double-Checked Locking is Broken\" Declaration documented this failure in 2000. Without volatile on the resource field, the JVM is free to reorder the field assignment and the constructor completion, which means thread B can see a non-null reference to a partially constructed Resource while thread A is still inside new Resource(). The outcome depends entirely on timing, so no test suite catches it reliably. The pattern dates back to a time when synchronized methods carried significant overhead, and the double-checked idiom was widely taught as a standard optimization. Modern JVMs have closed much of that performance gap, making the synchronized version both safer and fast enough that the performance argument for double-checked locking no longer holds.\n\nThe fix is to synchronize the method:\n\nIf method-level synchronization is too coarse, an inner static holder class achieves lazy initialization through the JVM's class-initialization guarantee, with no explicit synchronization needed:\n\nThe JVM guarantees that ResourceHolder is not initialized until getResource() is first called, and class initialization is inherently thread-safe per JLS 12.4, so this approach is both lazy and correct without any synchronization code.\n\nSynchronizing on value-based classes (S1860)\n\nThe next pattern is a fundamentally different kind of failure. The synchronization mechanism itself is unsound because the lock object isn't what the developer thinks it is.\n\nA private static final field used as a lock looks reasonable. The problem is that Boolean is a value-based class, and the JVM caches its instances. Every Boolean.FALSE reference in the entire application, including in third-party libraries, points to the same object in memory. Synchronizing on it means unrelated code paths can contend for the same lock, producing deadlocks with stack traces that show no logical connection between the contending threads.\n\nThe same applies to Integer.valueOf() within the cached range (-128 to 127), String literals, List.of() results, and java.time types. Two fields declared as Integer a = 0 and Integer b = 0 point to the same cached object, so synchronizing on a in one method and b in another creates a single shared lock where the developer intended two independent ones.\n\nThe fix is a dedicated Object instance:\n\nSleeping with a lock held (S2276)\n\nThread.sleep() pauses the current thread but does not release the monitor lock, so every other thread waiting to enter this synchronized block is frozen for the duration of the sleep. If another thread needs this lock before it can set the condition that makes ready() return true, you have a deadlock. This pattern appears naturally in polling loops and retry logic, where Thread.sleep() is the intuitive choice for introducing a delay.\n\nObject.wait() releases the lock while waiting, allowing other threads to make progress:\n\nThe distinction between sleep() and wait() is fundamental to Java concurrency, but it's also the kind of semantic difference that doesn't affect whether the code compiles or passes single-threaded functional tests. The signatures are similar, the behavior in a test with one thread is identical, and the bug only surfaces under real contention.\n\nWhy static analysis catches what tests miss\n\nTry writing a unit test that reliably catches double-checked locking. 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Discover why deep static analysis, control flow, and taint analysis are critical to preventing vulnerabilities and architectural decay in agent-centric development.","Nicolas Peru","2026-04-27T15:00:00Z",[12782,12818,12734],"Code quality",[],[12787,12821,12739],"Category:Code quality","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 13 - 4pm CEST | 9am CDT Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. Choosing the right code analysis for AI-assisted development\n\nTL;DR overview\n\nModern software teams require multilayered verification that goes beyond basic linting for AI-assisted development.\n\nMultilayered verification engines detect deep semantic bugs, like SQL injection, through control flow graphs.\n\nAutomated security tools mitigate supply chain risks by identifying malicious packages and hardcoded secrets.\n\nArchitecture-as-code enforcement prevents structural decay and technical debt caused by verbose, agent-generated code.\n\nAI coding tools have changed a basic assumption of software development. When an assistant can generate hundreds of lines of valid, well-formatted code in seconds, “the developer wrote it and reviewed it” no longer describes what actually happened. When an agent can modify 20 files across a service boundary in an hour, “the team reviewed the pull request” does not mean what it used to.\n\nThe risk profile has shifted along three axes simultaneously. The bugs are deeper. The attack surface is wider. The structural decay is faster. A linter addresses none of these. This article examines the three categories of risk that the Agent Centric Development Lifecycle (AC/DC) introduces and why each requires analysis that operates beyond pattern matching against syntax.\n\nWhat linters do, and where they stop\n\nA linter parses source code into an abstract syntax tree (AST) and applies rules against its structure. Rules operate as pattern matchers: they see what expressions exist and how they nest, but nothing about what the code does when it runs. This gives linters a well-defined and genuinely useful scope. They catch syntax errors, undefined variables, unused imports, style violations, and simple type mismatches. They run in milliseconds. For a team with no static analysis at all, adopting a linter produces immediate quality improvements.\n\nHowever, linters have no model of program execution, no representation of how values move through a system, and no ability to reason about what happens when function A in module B passes a value to function C in module D. That kind of analysis requires an analytical model that builds graphs of program behavior and reasons over them mathematically.\n\nA multilayered code verification engine like SonarQube operates at this mathematical reasoning level, covering issues from syntactic pattern matching through control flow, data flow, and taint analysis, in a single integrated platform. A team adopting it gets linting as one layer within a much deeper stack. \n\nThis allows development teams to address a range of critical security, maintainability, and reliability issues that would otherwise be missed. \n\nThree key risk vectors that linters miss:\n\n\n1. Deep bugs and vulnerabilities that syntax cannot reveal\n\nStatic analysis is a spectrum. Linting occupies the first level. Each level beyond it exists because there are real, consequential categories of bugs that the previous level cannot detect.\n\nControl flow analysis\n\nConsider a function where one branch initializes a database connection and another does not, but both paths later attempt to use that connection. Or a function where a condition is always true, making an entire else branch dead code that silently hides the logic it was supposed to provide. A linter sees individual statements. It does not model the relationships between them.\n\nDetecting these requires constructing a control-flow graph (CFG), a directed graph of every possible execution path through a function. That means every branch, loop, exception handler, and early return. The analysis engine walks each path to determine whether variables are initialized before use, whether conditions are satisfiable, and whether all branches are reachable. A CFG answers a question linting cannot: “what can this code actually do?”\n\nAI-generated code is particularly prone to these issues. AI coding tools frequently generate redundant guard clauses, impossible condition combinations, and dead branches that look plausible but never execute. The code reads well. It just does not behave as expected.\n\nData flow analysis\n\nA more dangerous category of bug emerges when the problem is not about which paths execute, but about what data does as it moves through them. Consider a function that receives a user object, extracts the user’s role, passes it through a formatting function, then uses it to construct a file path. Is the role validated before it reaches the file system call? The answer depends on tracing the value across multiple assignments and function boundaries.\n\nThis is data flow analysis: constructing a data-flow graph (DFG) that models how values are assigned, transformed, and consumed across functions, files, and modules. Without it, you cannot determine whether a null can propagate from a failed database lookup to a crash three function calls later, or whether two concurrent code paths are operating on stale vs. fresh copies of the same data. These bugs cause production incidents, and they are invisible at the AST level because the syntax at every individual point is perfectly valid.\n\nTaint analysis\n\nTaint analysis applies data flow reasoning specifically to security. The engine identifies sources (places where untrusted data enters: HTTP parameters, file contents, environment variables) and sinks (places where data is consumed dangerously: SQL queries, shell commands, file system operations). It then applies graph reachability algorithms to determine whether any execution path connects a source to a sink without passing through an adequate sanitizer. This is mathematical reasoning in the formal sense—the codebase is modeled as a graph, and properties of that graph are computed and checked against security conditions. The question is not “does this line look dangerous?” but “can untrusted data reach this dangerous operation through any sequence of calls?”\n\nConsider this Python function, exactly the kind of code an AI assistant might generate:\n\nA linter finds nothing wrong. The syntax is valid, the type hint is present, the f-string is well formed. SonarQube identifies username as a taint source (external input), traces it through the f-string interpolation into db.execute() (a SQL sink), determines no parameterization or sanitization occurs on that path, and raises a confirmed SQL injection vulnerability.\n\nThis example is simple enough that an experienced reviewer might catch it. Real taint flows are not. In a documented analysis of the OpenAPI Generator project, SonarQube traced a taint flow that propagated user-controlled data through 28 distinct steps across multiple files before reaching a dangerous file system operation, leading to the discovery of CVE-2024-35219, a","blogpost___gpt_5_5_s_biggest_blind_spot__copy_-0",{"_40":12825},{"_41":12749,"_12750":12751},{"_12723":12827,"_40":12840},{"_36":12828,"_89":12830,"_7281":12832,"_7312":12834,"_132":12836,"_7212":12838},{"_41":12813,"_12750":12751,"_12756":12829},[],{"_41":12814,"_12750":12751,"_12756":12831},[],{"_41":12815,"_12750":12751,"_12756":12833},[],{"_41":42,"_12750":12751,"_12756":12835},[],{"_41":7381,"_12750":12751,"_12756":12837},[],{"_41":12813,"_12750":12751,"_12756":12839},[],{"_41":12822,"_12750":12751,"_12756":12841},[],{"_187":12065,"_250":12843,"_12723":12844,"_12683":12851,"_237":12712,"_12740":12712,"_40":12853,"_12742":12743,"_12744":12854,"_12746":12855,"_12752":12857},"2026-04-27T20:52:48.0812447Z",{"_39":12725,"_394":12845,"_36":12846,"_89":12847,"_7281":12848,"_7312":42,"_132":7381,"_7212":12846,"_12730":7392,"_12731":12816,"_7337":7314,"_7327":12849,"_12735":12850},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/e48681ad-1a95-4637-8c3e-8a616785ebee/claude-opus-4-7-evaluation-blog-index.webp","Claude Opus 4.7: An evaluation review & metrics benchmarks","Discover how Claude Opus 4.7 cuts code volume by 40% but increases vulnerability risks. See the full technical audit of bugs, complexity, and code smells.","Prasenjit Sarkar",[415,2416,12782,12818],[],[12852,12786,12787,12821],"Category:SonarQube Cloud","Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 13 - 4pm CEST | 9am CDT Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. TL;DR overview\n\nClaude Opus 4.7 is Anthropic's flagship model, delivering 40% more concise code than version 4.6.\n\nEvaluation shows an 82.52% functional pass rate with significantly improved production-critical blocker bug density.\n\nHigh cognitive complexity and a 290 per mLOC vulnerability density require rigorous security reviews.\n\nFocus verification on increased cryptography misconfigurations and hard-coded credentials to ensure safe AI-generated code.\n\nClaude Opus 4.7 is Anthropic's latest flagship model. Using our proprietary LLM code quality and security evaluation framework, we discovered the new model delivers a clear efficiency improvement: 40% less code for the same functional pass rate as Opus 4.6 Thinking. That's what the data says at first glance. However, upon a closer look, the picture shifts. \n\nWhat was measured\n\nModel: Claude Opus 4.7 (Adaptive Thinking mode)\n\nLanguage: Java\n\nBenchmark: 4,444 tasks (HumanEval, MBPP, ComplexCodeEval)\n\nAnalyzer: SonarQube systematic code analysis. Density metrics are per 1,000 lines of code (kLOC); category breakdowns are per million lines (MLOC)\n\nTwo important terms to define before getting into the results:\n\nCyclomatic complexity: Counts independent paths through a function.\n\nCognitive complexity: This SonarQube metric weights nested and deeply branched logic more heavily, reflecting how difficult the code is for a human to read.\n\nNeither metric captures correctness. Both correlate with how long review and testing take.\n\nKey metrics at a glance\n\nVolume and style\n\nClaude Opus 4.7 produced 336,283 lines of code across 4,444 tasks. For the same tasks, Opus 4.6 Thinking produced 566,389 lines. Opus 4.7 produces 40% fewer lines for the same work. The functional pass rates are 82.52% and 82.55%. Same pass rate on the same tasks, fewer lines of code.\n\nComments dropped to 3.8% of the output, down from 8.2% in Opus 4.6. The code is more compact and less annotated. If you're maintaining this code past the immediate task, you'll have less inline context to work from.\n\nComplexity\n\nCognitive complexity per kLOC is 171.22 and cyclomatic complexity for Opus 4.7 is 240.63. Cognitive complexity is higher than Opus 4.6 Thinking's 132.1 per kLOC. The code is shorter but denser—more branching logic and nested control flow per thousand lines. When models write less code, they often pack more logic per line. Reviewing each line takes more effort, even though there are fewer total lines. Combined with a comment density of just 3.8%, independent and deterministic code reviews become more important than ever.\n\nBug density and severity\n\nBug density is 0.80 per kLOC. Severity breakdown per million lines:\n\nBlocker bugs are at 74 per MLOC, down from 83 in Opus 4.6. Critical bugs held at 48 per MLOC. These are the two levels that cause production fires, and both improved.\n\nConcurrency and threading bugs are at 131 per MLOC, tied with exception handling as the largest bug category in this evaluation. The rate is lower than Claude Opus 4.6's 157 per MLOC, but concurrent patterns remain the dominant area of risk.\n\nConcurrency bugs are expensive. They're hard to reproduce in testing, tend to be environment-dependent, and can produce intermittent failures that take significant time to diagnose. The rate here is better than the prior generation, but it remains the dominant bug category to watch.\n\nVulnerability density and severity\n\nVulnerability density is 0.29 per kLOC. Severity breakdown per million lines:\n\nBlocker and critical vulnerabilities went up compared to Opus 4.6, which had 53 and 56 per MLOC respectively. This is where the model regressed. Specific vulnerability categories:\n\nCryptography misconfigurations, which include weak algorithms, insecure key sizes, improper use of random number generators, are one of the more common failure modes in AI-generated code, and they show up here at 57 per MLOC. Path traversal and injection at 24 per MLOC is a category SonarQube catches reliably through data flow analysis. Hard-coded credentials are at 45 per MLOC, and XXE is at 39 per MLOC.\n\nMaintainability signals\n\nCode smell density is 23.01 per kLOC, driven primarily by collection and generics parameter type issues (3,565 per MLOC) and assignment, field, and scope visibility issues (2,132 per MLOC). These are cases where the model uses raw types instead of properly parameterized generics, or where field visibility is looser than it needs to be. In Java, these issues carry real cost: they suppress compiler warnings, make refactoring harder, and can mask bugs a properly typed implementation would catch at compile time.\n\nThe overall code smell number should be read alongside the comment density. At 3.8% comments across 336,000 lines, teams maintaining this code will find fewer signposts and more accumulated minor issues to address as the codebase ages.\n\nFunctional skill\n\nThe passing test rate for Opus 4.7 is 82.52%, with missing completions at 0.45%. The functional pass rate is essentially unchanged from Opus 4.6 82.55% — the generational update preserved functional capability. But 82.52% is also where verification earns its keep: roughly one in six generated solutions doesn't pass functional tests, and that rate isn't predictable in advance for any individual task. Testing pipelines catch what the model misses.\n\nWhat this means for developer teams using Opus 4.7 \n\nThe conciseness improvement is real. Roughly 40% less code for the same results means smaller review surfaces, faster iteration, and potentially lower token costs. Blocker bug density improved too, fewer issues that caused immediate production failures, continuing a positive trend from Opus 4.6.\n\nThe areas requiring active management are structural. Denser code, fewer comments, and higher per-line cognitive complexity raise the per-task cost of human review, even as total line count drops. The net review burden depends on how a team manages that tradeoff.\n\nThe vulnerability picture is the one that most deserves attention. Opus 4.7 ships fewer bugs than Opus 4.6, but more vulnerabilities, in a denser codebase with fewer comments. Fewer lines does not mean less security risk. The jump in blocker and critical vulnerabilities means security review cannot be treated as a checkbox. Systematic multilayered code analysis tools in your development pipeline, catching path traversal, cryptography misconfigurations, and hard-coded credentials at generation time, are the practical way to address this without adding manual review time.\n\nThree takeaways:\n\nConciseness is the headline improvement. Roughly 40% fewer lines for the same functional pass rate means smaller review surfaces and potentially lower token costs. This is a meaningful efficiency","blogpost___openai_gpt_5_5__an_evaluation__copy_-0",{"_40":12856},{"_41":12749,"_12750":12751},{"_12723":12858,"_40":12871},{"_36":12859,"_89":12861,"_7281":12863,"_7312":12865,"_132":12867,"_7212":12869},{"_41":12846,"_12750":12751,"_12756":12860},[],{"_41":12847,"_12750":12751,"_12756":12862},[],{"_41":12848,"_12750":12751,"_12756":12864},[],{"_41":42,"_12750":12751,"_12756":12866},[],{"_41":7381,"_12750":12751,"_12756":12868},[],{"_41":12846,"_12750":12751,"_12756":12870},[],{"_41":12853,"_12750":12751,"_12756":12872},[],{"_187":12066,"_250":12874,"_12723":12875,"_12683":12882,"_237":12712,"_12740":12712,"_40":12883,"_12742":12743,"_12744":12884,"_12746":12885,"_12752":12887},"2026-04-27T20:53:12.0538879Z",{"_39":12725,"_394":12876,"_36":12877,"_89":12878,"_7281":12848,"_7312":42,"_132":7381,"_7212":12877,"_12730":7392,"_12731":12879,"_7337":7314,"_7327":12880,"_12735":12881},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/52c0083b-fbe1-432f-8903-ac8be8444b47/crest-alpha-evaluation-blog-index.webp","OpenAI GPT-5.5: An evaluation","Is OpenAI's GPT-5.5 safe for production? Explore our independent 4,444-task Java benchmark. See why its security is top-tier but its 'verification debt' is high.","2026-04-23T15:00:00Z",[415,2416,12782,12818],[],[12852,12786,12787,12821],"Products SonarQube products Cloud-based static analysis tool for your CI/CD workflows Self-managed static analysis tool for continuous codebase inspection Free IDE extension that provides on-the-fly analysis and coding guidance Improve code produced by LLMs Early access Secure use of open source code with advanced SAST and SCA Bring code quality and security into your AI workflow Open betas Verify AI code as it's written by agents Guidance for coding agents from the first prompt Fix code issues at scale, on demand. Why Sonar Use cases Validate AI code for security and quality Secure apps and prevent vulnerabilities Ensure secure, high-quality code Remove friction and boost productivity Automate proof of code compliance Align AI and developer standards Catch code secrets in development Secure your software supply chain Explore Industries Customer recognition Developers For developers Documentation Integrations 40+ languages & frameworks Resources Get started Explore Research Company Learn about Sonar Join us! Upcoming events Webinar May 13 - 4pm CEST | 9am CDT Webinar May 27 - 4pm CEST | 9am CDT Global event May 12 - October 15th Table of contents TL;DR overview Start your free trial Verify all code. Find and fix issues faster with SonarQube. TL;DR overview\n\nOpenAI’s latest model, GPT-5.5, delivers some of the strongest security metrics we have analyzed to date.\n\nSecurity is a definitive strength for GPT-5.5, featuring a low vulnerability density of 75 per mLOC. \n\nWith a flat distribution across all severity levels, the model proves it isn't just avoiding simple catches.\n\nConcurrency remains a challenge across LLMs, as threading bugs at around 170 per mLOC dominate the overall profile.\n\nVerification debt compounds as high-volume, complex outputs outpace manual review, shifting the burden of proof to engineering.\n\nGPT-5.5 is the latest model from OpenAI, and it delivers huge improvements in a key area: security. In fact, its security numbers are some of the best we’ve seen. Vulnerability density is low, consistent across runs, and flat across severity levels. That's the headline. But, as with all models, there’s a more nuanced, complex story when we dig below the surface. \n\nWe ran GPT-5.5 through Sonar's LLM evaluation framework, which is designed to measure LLM-generated code against the same rules as a developer-written codebase.\n\nWhat was measured\n\nModel: GPT-5.5\n\nLanguage: Java\n\nBenchmark: 4,444 tasks\n\nRuns: 10 independent runs at temperature=1.0, reasoning_effort=medium\n\nAnalyzer: SonarQube systematic code analysis. Density metrics are per 1,000 lines of code (kLOC); category breakdowns are per million lines (mLOC). \n\nThere are two important terms to define before diving into the results: \n\nCyclomatic complexity: Counts independent paths through a function. \n\nCognitive complexity: This SonarQube metric weights nested and deeply branched logic more heavily, reflecting how difficult the code is for a human to read. \n\nNeither metric captures correctness. Both correlate with how long review and testing take.\n\nKey metrics at a glance\n\nVolume and style\n\nAcross the 4,444-task benchmark, GPT-5.5 generated 703,324 lines of code and only 2% of that output is comments. In practical terms, for every 100 lines a developer opens in review, roughly two contain any explanation. Comments are not strictly necessary for explanation, if functions and variables are named well this is also sufficient. That ratio compounds with the output volume—more code and less documentation means more cognitive load on anyone touching that code after generation.\n\nComplexity\n\nCognitive complexity is 151.8 per kLOC and cyclomatic complexity is 251.1 per kLOC. Cognitive complexity, as measured by SonarQube, tracks how difficult it is for a human to understand a piece of code. It penalizes nested conditionals, loops inside loops, and branching logic where a reader has to hold multiple states in their head. Code with high cognitive complexity is harder to review accurately, harder to write good tests for, and harder to modify without introducing new bugs.\n\n700,000 lines of elevated complexity, with minimal comments, create a review surface where errors are easier to miss. Independent and deterministic code reviews become more important than ever. \n\nBug density and severity\n\nOverall bug density is 0.52 per kLOC. The chart below details the severity distribution.\n\nBlockers and criticals are low (43 and 26 per million lines)and that's what matters most for production stability. But there's a long tail of major and minor issues: 232 and 220 per million lines that don't cause immediate failures but accumulate into technical debt, slow down future changes, and occasionally surface as bugs once the codebase evolves. At GPT-5.5’s output volume, that adds up quickly.\n\nThe one category worth calling out is concurrency and threading bugs, at 170 per mLOC. That's substantially higher than any other bug category, as evidenced in the chart below.\n\nConcurrency bugs are expensive. They're hard to reproduce in testing, tend to be environment dependent, and can produce intermittent failures requiring significant time to diagnose. The elevated rate here is consistent with a model that is generating more code and more concurrent patterns as part of that volume.\n\nVulnerability density and severity\n\nVulnerability density is 75 per mLOC. This is one of the cleaner security profiles we've seen. The severity breakdown holds up at every level, as seen in the chart below.\n\nThe distribution is flat. Blockers and criticals aren't disproportionately high relative to major and minor, so the model isn't just avoiding trivially detectable issues while leaving deeper ones in place. \n\nTop vulnerability categories shown below (covering roughly 43% of total vulnerabilities; the balance spreads across smaller categories). \n\nCryptography misconfigurations—weak algorithms, insecure key sizes, improper use of random number generators—are one of the more common failure modes in AI-generated code. But at 17 per mLOC, GPT-5.5 keeps that category manageable with automated detection. Path traversal and injection is particularly low at 7 per mLOC. Security is a clear strength, and the consistency of the numbers makes that credible.\n\nMaintainability signals\n\nCode smell density is 17.1 per kLOC, driven primarily by collection and generics parameter type issues. Cases where the model uses raw types instead of properly parameterized generics, or where collection handling bypasses type safety in ways that don't cause immediate failures but create technical friction over time. In Java, these issues carry real cost: they suppress compiler warnings, make refactoring harder, and can mask bugs a properly typed implementation would catch at compile time.\n\nThe overall code smell number is not trivial, but it should be read alongside the output volume. A density of 17.1 per kLOC across 700,000 lines is a larger absolute number than the same density across a more concise output. Combined with comment density of 2%, teams maintaining this code will find fewer signposts and more accumulated minor issues to address as the codebase ages.\n\nFunctional skill\n\nThe passing test rate is 78.7%, with missing completions at just 0.18%, which means the model completes tasks reliably. But the 78.7% pass rate is where verification earns its keep: more than one in five generated solutions doesn't pass functional tests, and that rate isn't predictable in advance for any individual task. Code review and testing pipelines catch what the model misses.\n\nWhat this means for developer teams using GPT-5.5\n\nGPT-5.5's security numbers are lower, stable, and flat across severity levels. For teams where security is a primary acceptance criterion for AI generated code, those numbers matter.\n\nThe areas requiring active management are more structural than any specific bug type. 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