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java.time bugs that don’t throw exceptions","Learn how SonarQube detects java.time bugs that compile cleanly but cause wrong timezone math, flaky tests, and bad comparisons.","Killian Carlsen-Phelan, Jean Jimbo","Blog post","cta","Read article","creationDate","2026-06-09T16:00:00Z",[2412,415,10647],"Languages & frameworks","keywords",[],[10651,10652,10653],"Category:AI","Category:SonarQube Cloud","Category:Languages & frameworks","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 Secure use of open source code with advanced SAST and SCA AI code review that fixes issues and commits only when your build passes. New Bring code quality and security into your AI workflow Improve code produced by LLMs Early access 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 June 11, 2026 | 10:00 am CT Webinar Wednesday, June 17 at 4pm CEST | 9am CDT Webinar Thursday, June 25 at 5pm CEST | 10am CT 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\nSonarQube's new java.time rules catch bugs that compile, pass tests, and produce silently wrong results such as duration math across timezones, non-deterministic clocks, and identity comparison on value types.\n\nLocalDateTime duration calculations across timezone boundaries return plausible numbers that survive code review but skew billing, SLA, and scheduling systems.\n\nAI coding tools default to LocalDateTime because it's the simpler type, and rarely inject a Clock because the pattern requires a design decision absent from most training data.\n\nAll eight rules are present in Sonar Way and active by default on SonarQube Cloud, with SonarQube Server shipping them in 2026.4.\n\nA flight leaves New York at 10 PM and lands in Paris at 11 AM the next morning, and you compute the elapsed time with java.time.\n\nThe silent wrong answer\n\nThe method returns 13 hours for a flight that took seven, because ChronoUnit.HOURS.between on two LocalDateTime values does clock-face subtraction: 11:00 minus 22:00 the day before is 13 hours. The calculation has no reason to account for timezones because LocalDateTime carries no timezone information (that's what \"Local\" means in the class name). Departed New York in EDT (UTC-4), arrived in Paris in CEST (UTC+2), and the six-hour offset difference between the two zones is invisible to the calculation. Tests that assert hours > 0 pass, and the wrong number flows into billing systems, SLA timers, or scheduling logic without triggering an exception.\n\nThe pattern appears anywhere duration math crosses timezone boundaries. A billing system computes hours between a session start in one timezone and end in another; the wrong duration inflates or deflates the invoice by a fraction that looks like rounding, not a bug. SLA monitoring across globally distributed services makes the same mistake, skewing response times in whichever direction the offset difference dictates. The code doesn't crash but returns a number plausible enough to survive code review and specific enough to cause real damage downstream.\n\nSonarQube rule S8700 flags Duration.between() and ChronoUnit.X.between() when both operands are LocalDateTime, because the result is a calendar delta rather than physical elapsed time. The fix converts both timestamps to ZonedDateTime with explicit zones:\n\nThe six-hour offset between EDT and CEST accounts for the entire discrepancy between the two calculations.\n\njava.time's type system is not oblivious to the timezone boundary problem, and if you try to convert a LocalDateTime to an Instant directly, the API throws DateTimeException at runtime because the conversion requires timezone information that the local type doesn't carry. Rule S8220 catches these conversions statically before they crash in production, since java.time is designed to fail loud at the boundary between local and timezone-aware types. Duration.between is where it fails quiet instead, because both operands are LocalDateTime and the type system has no boundary to enforce when they already share a type. The method cannot distinguish two wall-clock readings in the same zone from two readings in different zones, which is why S8700 exists: LocalDateTime arithmetic is a domain where the type system permits operations that the physical world doesn't.\n\nS8220's false-positive handling is careful about this, suppressing findings in test code that intentionally triggers DateTimeException and supporting JUnit 4, TestNG, JUnit 5 assertThrows, AssertJ, and try-catch-fail patterns. Google's Error Prone catches overlapping patterns in the same space (FromTemporalAccessor covers the same ground as S8220, MisusedWeekYear overlaps with S3986), which confirms these as industry-validated bug categories rather than Sonar-specific opinions.\n\nLLMs default to LocalDateTime because it's the simpler type, and GitHub Copilot's CLI has an open bug (at time of writing) where its tag reports UTC regardless of the system timezone.\n\nWhen your tests lie about time\n\nTwo consecutive Instant.now() calls can return identical values on modern hardware because the system clock's resolution means both calls sample the same instant. The non-determinism compounds with JVM behavior, leading to a @RepeatedTest passing on the first iteration while the JVM is cold, then failing on subsequent iterations as the warmed-up code executes fast enough to collapse both timestamps into a single reading. The test is correct in isolation, but because the unreliability depends on clock precision, CPU load, and JVM warmup state, it manifests differently across development machines and CI environments.\n\nThe underlying problem mirrors the duration bug, because java.time's type system lets you call .now() without a Clock even though deterministic testing requires explicit control over time. Rule S8692 flags .now() calls without a fixed clock in test code, and since the fix requires injecting a Clock that tests can control, it's an architectural change rather than a quick patch.\n\nS8692's quick fix is explicitly marked \"infeasible\" because fixing requires dependency injection, not a search-and-replace. The one-hour remediation estimate (vs. five minutes for most other rules in this set) reflects the real cost of retrofitting testable time into code that wasn't designed for it.\n\nS8692 carries INFO severity because nearly every test suite calls .now() without a Clock, and flagging all of them at higher levels produces too much noise. AI-generated test code rarely injects a Clock because the pattern requires a design decision that doesn't appear in most training examples.\n\nWhen equals isn't ==\n\n== on Instant compares object references rather than values, so two variables that hold the same point in time but live at different heap addresses will compare as unequal even though they represent identical moments. As with the duration and clock bugs, java.time's type system permits an operation that the domain makes meaningless for value-based types. The pattern is even harder to catch on the primitive wrappers that S8696 also covers, because the JVM's caching behavior makes == intermittently correct: Integer.valueOf(100) == Integer.valueOf(100) returns true because the JVM caches integers in the -128 to 127 range, but increase the value past 127 and == starts returning false. A test that exercises small values passes, a production workload with larger values fails, and the defect appears to be environment-dependent rather than logic-dependent.\n\nRule S8696 was born from the java.time investigation but generalized to cover every valu","source","sonarsource.com","objectID","blogpost___how_sonarqube_traces_a_sql_injection_yo-0","_snippetResult",{"_40":10662},{"_41":10663,"_10664":10665},"Products SonarQube products Cloud-based static analysis tool for your …","matchLevel","none","_highlightResult",{"_10634":10668,"_40":10682},{"_36":10669,"_89":10672,"_7276":10674,"_7307":10676,"_132":10678,"_7207":10680},{"_41":10638,"_10664":10665,"_10670":10671},"matchedWords",[],{"_41":10639,"_10664":10665,"_10670":10673},[],{"_41":10640,"_10664":10665,"_10670":10675},[],{"_41":42,"_10664":10665,"_10670":10677},[],{"_41":10641,"_10664":10665,"_10670":10679},[],{"_41":10638,"_10664":10665,"_10670":10681},[],{"_41":10655,"_10664":10665,"_10670":10683},[],{"_187":10685,"_250":10686,"_10634":10687,"_10593":10697,"_237":10622,"_10654":10622,"_40":10701,"_10656":10657,"_10658":10702,"_10660":10703,"_10666":10705},"blogpost___jellyfin_remote_code_execution__copy__b67f633","2026-06-09T18:42:09.0280595Z",{"_39":10636,"_394":10688,"_36":10689,"_89":10690,"_7276":10691,"_7307":42,"_132":10641,"_7207":10689,"_10642":10643,"_10644":10692,"_7396":7393,"_7332":7309,"_7322":10693,"_10648":10696},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/68af460e-c876-47da-aba0-504de853b06b/sql-injection-learn-page-landscape-2x.webp","How SonarQube traces a SQL injection your AI coding agent produced","Learn how SonarQube traces SQL injection across Spring Boot files using taint analysis to expose unsafe database queries from user input.","Killian Carlsen-Phelan","2026-06-03T16:00:00Z",[2412,10694,10695,236,415],"Vulnerability research","Code security",[],[10651,10698,10699,10700,10652],"Category:Vulnerability research","Category:Code security","Category:SonarQube Server","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 Secure use of open source code with advanced SAST and SCA AI code review that fixes issues and commits only when your build passes. New Bring code quality and security into your AI workflow Improve code produced by LLMs Early access 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 June 11, 2026 | 10:00 am CT Webinar Wednesday, June 17 at 4pm CEST | 9am CDT Webinar Thursday, June 25 at 5pm CEST | 10am CT 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\nSonarQube's taint analysis traces data from where it enters an application to where it reaches a dangerous operation, catching injection vulnerabilities across file and method boundaries.\n\nAI coding agents reproduce SQL injection patterns from training data, and the code generation loop does not follow data across call boundaries to catch these patterns.\n\nThe execution flow view annotates each hop in the taint chain, showing exactly how user-controlled data reaches the vulnerable database call.\n\nThe same source-to-sink model applies to every taint-traced finding, from XSS to path traversal, making the capability transferable.\n\nSQL injection has been in the OWASP Top 10 for over a decade, and AI coding agents keep producing it. Invicti analyzed Copilot's security suggestions and found that likely because the model is trained on public repositories including non-production code, its suggestions can reproduce the same insecure patterns. The injection path continues to exist despite code compiling and tests passing because nothing in the generation loop follows the data across method call boundaries.\n\nSonarQube's taint engine catches these by building a data flow graph of every assignment, method call, and parameter passing in your code, then following the data from where it enters the application to where it gets used in a dangerous operation. Below we will trace a single SQL injection finding across three Spring Boot files, from the HTTP request parameter where tainted data enters to the database call where it arrives unsanitized.\n\nPrerequisites\n\nA SonarQube Cloud account (any plan, including Free) or SonarQube Server Developer Edition or higher. Note: the Free plan analyzes main-branch code only. See plans and pricing. SonarQube Community Build does not support taint analysis and has very limited security coverage. \n\nJava 17 or higher, Maven\n\nThe taint chain\n\nThe app we’re using is a minimal Spring Boot 3 API with a single endpoint: GET /users/search?name=.... The vulnerability arises from the following three classes:\n\nUserController.java accepts the request parameter and passes it to the service:\n\nUserService.java passes it straight to the repository:\n\nUserRepository.java uses the value to build a SQL string:\n\nTo both human eyes and AI, the controller and service look unremarkable in isolation. UserService.java is a standard pass-through; nothing in that method necessarily signals a problem because the problem isn't there. The string concatenation in UserRepository.java is where the vulnerability lives, but you'd only know that name was dangerous if you knew it came from an HTTP request parameter two method calls away. Unfortunately, static analysis that examines files in isolation can’t make that connection.\n\nSonarQube’s finding\n\nAfter scanning, SonarQube returned one issue: rule javasecurity:S3649 at BLOCKER severity, with Security impact, mapping to CWE-89 and OWASP Top 10 2021 A03.\n\nThe finding lands on jdbcTemplate.query() in UserRepository.java, which is the right location. However, this finding alone doesn't explain how SonarQube knew that the name argument to searchByName() was user-controlled. In order to find that out, you have to look in the execution flow.\n\nReading the execution flow\n\nIn the left sidebar, you'll see \"1 execution flow\"; click it and SonarQube expands 12 numbered steps (in this example), grouped by file.\n\nStep 1 is labeled SOURCE: \"a user can craft an HTTP request with malicious content.\" That annotation sits on the @RequestParam String name declaration in UserController.java. SonarQube recognizes Spring MVC's @RequestParam as a taint source, an entry point where externally supplied data enters the application, and marks everything that flows from it as potentially tainted.\n\nSteps 2 and 3 document the parameter passing through the controller. Steps 4, 5, and 6 track it through UserService.findUsersByName(), a single-line pass-through that takes the tainted name and hands it to the repository. SonarQube continues to follow along because taint analysis keeps going as long as the data keeps moving, unhindered by method boundaries.\n\nStarting at step 9, each annotation in UserRepository.java describes what's happening to the tainted value:\n\nStep 9: \"The malicious content is concatenated into the string\"\n\nStep 10: \"This concatenation can propagate malicious content to the newly created string\"\n\nStep 11: \"A malicious value can be assigned to variable sql\"\n\nStep 12 is labeled SINK: \"this invocation is not safe; a malicious value can be used as argument.\" That's jdbcTemplate.query(sql, ...) on line 21, the database call where the tainted string gets executed.\n\nThe 12 steps serve as proof that begins with user-controlled data entered at @RequestParam, traveled through findUsersByName() unchanged, got concatenated into a SQL string, and arrived at jdbcTemplate.query() without ever being sanitized. A scanner that only read UserRepository.java in isolation might flag the concatenation, but it couldn't confirm that name was actually user-controlled, which SonarQube is able to do because it traced the data across all three files.\n\nWhat can the injection do?\n\nOpening the issue shows three impact categories in the \"Why is this an issue?\" tab: specifically, identity spoofing, data manipulation and deletion, and in database configurations with elevated permissions, remote code execution. The finding maps to CWE-89 and OWASP Top 10 A03 (Injection), which has remained on the OWASP list for over a decade because this pattern keeps surfacing in production code.\n\nFixing the taint chain\n\nThe \"How can I fix it?\" tab auto-detects your framework (SonarQube selected Spring for this project) and covers Hibernate, Java JDBC API, Couchbase, and the Spring Data drivers for Cassandra and Neo4j.\n\nThe fix for searchByName() is a one-line change:\n\nThe tab explains that when you use a prepared statement, the database server compiles the query logic before the application passes the actual values. The ? placeholder becomes a parameter, the query structure is frozen at that point, and whatever string arrives as name gets treated as data rather than SQL. An attacker can inject '; DROP TABLE users;-- and it won't execute as the database treats it as a literal string value, not an instruction.\n\nSonarQube traced the path from source to sink, told you what to fix and where, identified your framework, and surfaced the fix pattern, all without you having to leave the issue page.\n\nWhat the next taint finding looks like\n\nEvery taint-traced finding has a structure consisting of a ","blogpost___jellyfin_remote_code_execution__copy__b67f633-0",{"_40":10704},{"_41":10663,"_10664":10665},{"_10634":10706,"_40":10719},{"_36":10707,"_89":10709,"_7276":10711,"_7307":10713,"_132":10715,"_7207":10717},{"_41":10689,"_10664":10665,"_10670":10708},[],{"_41":10690,"_10664":10665,"_10670":10710},[],{"_41":10691,"_10664":10665,"_10670":10712},[],{"_41":42,"_10664":10665,"_10670":10714},[],{"_41":10641,"_10664":10665,"_10670":10716},[],{"_41":10689,"_10664":10665,"_10670":10718},[],{"_41":10701,"_10664":10665,"_10670":10720},[],{"_187":10722,"_250":10723,"_10634":10724,"_10593":10732,"_237":10622,"_10654":10622,"_40":10733,"_10656":10657,"_10658":10734,"_10660":10735,"_10666":10737},"blogpost___now_available__sonarqube_plugin_for_git","2026-06-09T18:36:52.5170617Z",{"_39":10636,"_394":10725,"_36":10726,"_89":10727,"_7276":10728,"_7307":42,"_132":10641,"_7207":10726,"_10642":10643,"_10644":10729,"_7396":7393,"_7332":7309,"_7322":10730,"_10648":10731},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/9a64e2aa-9d56-41c8-adda-270e0632af34/jellyfin-blog-index-2x.webp","Jellyfin remote code execution: Inconsistent validation leads to argument injection","Explore a Jellyfin remote code execution flaw where inconsistent validation enables FFmpeg argument injection and unauthenticated code execution.","Paul Gerste","2026-06-02T16:00:00Z",[10694],[],[10698],"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 Secure use of open source code with advanced SAST and SCA AI code review that fixes issues and commits only when your build passes. New Bring code quality and security into your AI workflow Improve code produced by LLMs Early access 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 June 11, 2026 | 10:00 am CT Webinar Wednesday, June 17 at 4pm CEST | 9am CDT Webinar Thursday, June 25 at 5pm CEST | 10am CT 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\nA Jellyfin argument injection vulnerability (CVE-2026-35033) allows unauthenticated attackers to execute arbitrary code on instances prior to version 10.11.7.\n\nInconsistent validation bypasses regex checks when transcoding options are parsed from semicolon-separated query parameters.\n\nAttackers can manipulate FFmpeg command line arguments to read and write arbitrary files using a known video ID.\n\nWriting shellcode to the .NET JIT compiler doublemapper memfd virtual file achieves code execution.\n\nJellyfin is a popular open-source media system written in C# with over 50,000 stars on GitHub. Users can manage their media library and stream media to various clients. To ensure compatibility and smooth playback, Jellyfin integrates with FFmpeg for on-the-fly media transcoding.\n\nWhile reviewing the code base, we discovered a new argument injection vulnerability that is a variant of previous FFmpeg argument injection flaws (CVE-2025-31499, CVE-2023-49096). While many user-controllable parameters are now validated in Jellyfin, we identified a validation bypass that allows an attacker to inject arbitrary arguments into the FFmpeg command line.\n\nIn this blog post, we dive into the technical details of the vulnerability, cover the exploitation path, present a new attacker primitive to turn file writes into code execution in .NET environments, and take a look at how this vulnerability was patched.\n\nImpact\n\nThe vulnerability we discovered is tracked as CVE-2026-35033 and affects Jellyfin versions before 10.11.7. The vulnerability is an argument injection in a media playback API endpoint and can allow unauthenticated attackers to execute arbitrary code on vulnerable Jellyfin instances.\n\nThe only requirement for an attacker is that they need to know a valid video ID. This can easily be obtained by authenticated attackers by retrieving one from the API. Unauthenticated attackers can still reach the vulnerable endpoint but it is harder for them to obtain a valid video ID. However, video IDs are not random but derived from the file path of the underlying media file. If an attacker can guess a path, they can compute a valid video ID and exploit the vulnerability.\n\nTechnical Details\n\nJellyfin allows users to manage and view their own media library. To provide smooth playback compatible with many clients, Jellyfin integrates with FFmpeg to transcode media files on the fly. The client can set some transcoding options, but these are limited to a safe subset. The playback API endpoint at /Videos//stream can be reached without authentication, but it requires knowledge of a valid ID. Such IDs can either be obtained by authenticated users via the API, but they are also guessable since they are essentially derived from the media file's path.\n\nMalicious transcoding options\n\nWhen an attacker knows such an ID, they can request the video file along with some transcoding options. The transcoding options can be passed in two different ways: as individual query parameters, or as a single params query parameter where many values are semicolon separated:\n\nFor many of the individual query parameters, custom attributes are used to limit them to sane values:\n\nIn this example, the LevelValidationRegex makes sure that level can only be a floating point number. However, these limits are not enforced in the same way when the values are parsed from the semicolon-separated params. The string value of this combined parameter is passed to StreamingHelpers.ParseParams() which unpacks the semicolon-separated list into the respective fields of a VideoRequestDto object. For field 15, the value is assigned to the Level field:\n\nAs we can see, no regex validation is performed in this path but the fully user-controlled string value lands in the same Level field as the regex-validated level query parameter would. From here, the string flows through some more parsing logic where it is eventually converted into a value FFmpeg understands:\n\nIf the user-provided value cannot be parsed as a double, the original value is passed on. This is a double-edged sword: it makes Jellyfin compatible with all kinds of values that FFmpeg might understand, but it also allows user-controlled content to flow deeper into the system. At the final step, the level value is added to the list of arguments passed to FFmpeg. There are several cases for well-known values, but there's again a fallback clause that passes on the original, user-controllable value:\n\nOn the first look, it seems that this might be a command injection vulnerability. However, due to the way Jellyfin and .NET pass the argument string to the system, it is not evaluated in a shell context. An attacker is therefore unable to append additional commands or inject subshells, but can still inject additional FFmpeg arguments!\n\nFrom argument injection to impact\n\nFFmpeg is a very versatile piece of software. It supports a wide variety of formats and operations, so it comes with an even greater number of arguments to configure them. For example, FFmpeg can't just process files but also network sources, making it possible to send and receive data.\n\nReading arbitrary files. To exfiltrate files from the system, an attacker could tell ffmpeg to read a file and copy it to a network destination by injecting the following arguments:\n\nThe -f null /dev/null part at the beginning makes sure that the arguments preceding the injection points do not mess with the attacker operation by redirecting it to /dev/null via the null format. The -map 0:0 -map 0:1 part at the end makes sure that any subsequent arguments after the injection point will correctly refer to the original options, preventing FFmpeg from aborting due to errors. The core of the injected arguments tells FFmpeg to use the data format so the file content is not parsed (-f data), where to read the data from (-i /etc/passwd), to only copy the data instead of converting it (-c copy), and to send the resulting output to a network destination (tcp://:).\n\nWriting arbitrary files. In the same way, an attacker can write arbitrary files by instructing FFmpeg to copy data from a network source into an attacker-controlled file path:\n\nHere, the network address becomes the source while the file path becomes the destination. The attacker can, for example, host the file content on an HTTP server that FFmpeg will download the file from.\n\nExecuting arbitrary code. A file write primitive is usually powerful enough to let an attacker execute arbitrary code, for example by writing webshells or overwriting code files. This is also poss","blogpost___now_available__sonarqube_plugin_for_git-0",{"_40":10736},{"_41":10663,"_10664":10665},{"_10634":10738,"_40":10751},{"_36":10739,"_89":10741,"_7276":10743,"_7307":10745,"_132":10747,"_7207":10749},{"_41":10726,"_10664":10665,"_10670":10740},[],{"_41":10727,"_10664":10665,"_10670":10742},[],{"_41":10728,"_10664":10665,"_10670":10744},[],{"_41":42,"_10664":10665,"_10670":10746},[],{"_41":10641,"_10664":10665,"_10670":10748},[],{"_41":10726,"_10664":10665,"_10670":10750},[],{"_41":10733,"_10664":10665,"_10670":10752},[],{"_187":10754,"_250":10755,"_10634":10756,"_10593":10764,"_237":10622,"_10654":10622,"_40":10766,"_10656":10657,"_10658":10767,"_10660":10768,"_10666":10770},"blogpost___jellyfin_remote_code_execution__copy_","2026-06-09T18:31:22.0508972Z",{"_39":10636,"_394":10757,"_36":10758,"_89":10759,"_7276":10760,"_7307":42,"_132":10641,"_7207":10758,"_10642":10643,"_10644":10729,"_7396":7393,"_7332":7309,"_7322":10761,"_10648":10763},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/0f453116-4b39-46b8-be25-b40386e8ec07/sonar-github-agentic-app-blog-landscape.webp","Now available: SonarQube Agent App in GitHub","Learn how the SonarQube Agent App brings code quality and security checks directly into GitHub agent workflows for faster feedback.","Brooks Naylor",[2412,10762],"Partner integrations",[],[10651,10765],"Category:Partner integrations","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 Secure use of open source code with advanced SAST and SCA AI code review that fixes issues and commits only when your build passes. New Bring code quality and security into your AI workflow Improve code produced by LLMs Early access 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 June 11, 2026 | 10:00 am CT Webinar Wednesday, June 17 at 4pm CEST | 9am CDT Webinar Thursday, June 25 at 5pm CEST | 10am CT 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. What is the SonarQube Agent App in GitHub?\n\nGitHub is expanding its agentic platform beyond coding agents to other external applications, including code verification and analysis agents. The SonarQube Agent App packages SonarQube’s shared skills and MCP server configuration as an Agentic App. Once installed, GitHub gains access to SonarQube's code quality and security analysis, all governed by your existing quality profiles and gates.\n\nThis is a big deal. The SonarQube Agent App brings code verification directly into the GitHub agentic workflow, so issues are caught and resolved right where the code is written, not minutes or hours later in a pipeline. That means faster feedback loops, fewer broken builds, and a verification step that actually keeps pace with autonomous agents.\n\nHow does the SonarQube Agent App work in GitHub?\n\nThe SonarQube Agent App is @-mentionable in issues and PRs, assignable to tasks, and visible in Mission Control. When invoked, it authenticates via OIDC, runs code verification against your quality gate, surfaces findings, and opens a session where you can check the agent’s work. Coding agents remediate from there, closing the Guide-Verify-Solve loop.\n\nWhat is Agent Centric Development and why does it matter for AI coding agents?\n\nCoding agents on GitHub now open PRs, respond to issues, and run on schedules, often with no human in the loop. CI catches some issues, but only after the fact. When pull requests are 10x larger and the code is generated by a probabilistic model, after-the-fact review can't keep up.\n\nThat's exactly what the Agent Centric Development Cycle (AC/DC) is built for. Because AI is probabilistic, code verification has to be deterministic, and it has to happen where agents work, not downstream in a pipeline. The SonarQube Agent App is the Verify step for agentic development on GitHub, bringing deterministic analysis, quality profiles, issue remediation, and shared skills to the platform.\n\nFor enterprises running multiple coding agents, this is especially significant. Every agent produces code to its own implicit quality bar. The SonarQube Agent App applies one consistent standard across all of them: one SonarQube instance, one set of rules, one quality gate from sandbox to merge. And developers who verify with SonarQube are 44% less likely to report outages due to AI-generated code.\n\nHow do I get started with the SonarQube Agent App for GitHub?\n\nThe SonarQube Agent App in GitHub is available for SonarQube Cloud customers. Install it on your GitHub organization or repos, just like any GitHub Agent App. \n\nInstall the Agentic App on your organization, then set two Copilot variables (COPILOT_MCP_SONARQUBE_ORG and COPILOT_MCP_SONARQUBE_PROJECT_KEY) at the repository, organization, or enterprise level. Sessions will reach SonarQube Cloud automatically.\n\nGet started with SonarQube Cloud Build trust into every line of code\n\nIntegrate SonarQube into your workflow and start finding vulnerabilities today.\n\n 4.6 / 5 Solutions Products Pricing Company Media Resources Compare Knowledge © 2026 SonarSource Sàrl. All rights reserved. Follow SonarSource on Twitter https://twitter.com/sonarsource Follow SonarSource on Linkedin https://www.linkedin.com/company/sonarsource/","blogpost___jellyfin_remote_code_execution__copy_-0",{"_40":10769},{"_41":10663,"_10664":10665},{"_10634":10771,"_40":10784},{"_36":10772,"_89":10774,"_7276":10776,"_7307":10778,"_132":10780,"_7207":10782},{"_41":10758,"_10664":10665,"_10670":10773},[],{"_41":10759,"_10664":10665,"_10670":10775},[],{"_41":10760,"_10664":10665,"_10670":10777},[],{"_41":42,"_10664":10665,"_10670":10779},[],{"_41":10641,"_10664":10665,"_10670":10781},[],{"_41":10758,"_10664":10665,"_10670":10783},[],{"_41":10766,"_10664":10665,"_10670":10785},[],{"_187":10787,"_250":10788,"_10634":10789,"_10593":10796,"_237":10622,"_10654":10622,"_40":10797,"_10656":10657,"_10658":10798,"_10660":10799,"_10666":10801},"blogpost___sonarqube_remediation_agent_wins_best_i","2026-06-09T18:26:47.7767268Z",{"_39":10636,"_394":10790,"_36":10791,"_89":10792,"_7276":10760,"_7307":42,"_132":10641,"_7207":10791,"_10642":10643,"_10644":10793,"_7396":7393,"_7332":7309,"_7322":10794,"_10648":10795},"https://assets-eu-01.kc-usercontent.com:443/ef593040-b591-0198-9506-ed88b30bc023/62b41537-bc91-4dc4-b348-c25b20158acd/github-copilot-cli-blog-landscape-2x.webp","Now available: SonarQube plugin for GitHub Copilot CLI","Connect GitHub Copilot CLI to SonarQube for quality gates dependency risk checks coverage insights and agent driven analysis.","2026-06-01T16:00:00Z",[2412,10762],[],[10651,10765],"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 Secure use of open source code with advanced SAST and SCA AI code review that fixes issues and commits only when your build passes. New Bring code quality and security into your AI workflow Improve code produced by LLMs Early access 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 June 11, 2026 | 10:00 am CT Webinar Wednesday, June 17 at 4pm CEST | 9am CDT Webinar Thursday, June 25 at 5pm CEST | 10am CT 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\nThe SonarQube plugin for GitHub Copilot CLI brings quality gates, issue scanning, and Agentic Analysis directly into your terminal workflow.\n\nThis integration connects your terminal-driven workflow to the SonarQube analysis engine using the SonarQube MCP Server or CLI.\n\nIt enables a deterministic verification layer during AI-driven development, allowing AI agents to find and fix code issues automatically.\n\nThe plugin includes secrets-scanning hooks to block the reading, writing, or pasting of sensitive credentials during coding sessions.\n\nThe SonarQube plugin for GitHub Copilot CLI is now available. It brings quality gate checks, issue scanning, dependency risk assessments, secrets detection, and SonarQube Agentic Analysis directly into your terminal workflow. If you're building with Copilot CLI, your AI-generated code can now be automatically verified before it leaves your machine.\n\nWhat is the SonarQube plugin for GitHub Copilot CLI?\n\nThe SonarQube plugin for GitHub Copilot CLI connects your terminal-driven workflow to the SonarQube analysis engine, which can be accessed either through our MCP server or our SonarQube CLI. \n\nHow the plugin works\n\nOnce configured, you get /sonarqube: slash commands inside your Copilot CLI session to list issues, check test coverage, verify quality gate status, and assess dependency risks. No browser. No context-switching.\n\nThe core of the integration is the sonar integrate copilot command. This command handles the complex configuration of MCP server entries in your .mcp.json, and the installation of the hooks.json and sonarqube.instructions.md required for secrets scanning and Agentic Analysis. Once you configure the integration, the SonarQube CLI handles container runtime detection and keychain handoff automatically.\n\nWhy you should care\n\nAs AI agents take on more code generation, the developer's job shifts toward verification and review. That's the heart of the Agent Centric Development Cycle (AC/DC). But relying on an AI to self-correct is non-deterministic. You need a verification layer that is consistent, automatic, and built into the coding loop.\n\nOnce SonarQube Agentic Analysis is configured, the Copilot CLI agent doesn't just write code and move on. After every file write, it runs sonar analyze agentic, reads the findings, fixes the issues, and re-runs the analysis until the file comes back with no remaining problems. In testing against a real codebase, the agent caught and resolved multiple issues on its first pass and only finalized the file once verification was complete. All in one terminal session.\n\nSonarQube's analysis is deterministic, comprehensive, and repeatable. Same code, same result, every time. That's a fundamentally different level of assurance than asking an LLM to review its own work. And since the analysis runs within the agentic coding loop, issues get found and fixed before the code even enters the PR flow.\n\nThe plugin also installs a secrets-scanning hook that blocks the agent from reading or writing files containing credentials, and instructs the agent to refuse prompts that paste sensitive content directly into the conversation. \n\nGet started now\n\nThe plugin is available today and can be configured in a few minutes.\n\nPrerequisites\n\nAn active GitHub Copilot subscription and the GitHub Copilot CLI installed.\n\nA SonarQube Cloud or SonarQube Server account.\n\nA local container runtime (Docker, Podman, or Nerdctl) to host the Sonarqube MCP Server.\n\nStep 1: Install the plugin\n\nRun these commands in your Copilot CLI session to add the marketplace and install the SonarQube plugin:\n\nStep 2: Run the integration\n\nInvoke the plugin’s integration skill to automate the installation of the SonarQube CLI, authentication to SonarQube, and configuration of the SonarQube MCP Server:\n\nThis command will walk you through the login process (using sonar auth login) and configure your .mcp.json with a sonarqube entry.\n\nStep 3: Verify and code\n\nRestart your Copilot CLI session to load the new configuration. You can now use /sonarqube: slash commands to list issues, check test coverage, or verify your quality gate status, introducing the deterministic verification layer to the agent’s workflow.\n\nBack your AI-driven development with the industry standard for code verification. Install the SonarQube plugin for GitHub Copilot CLI and start building code you can trust. /plugin marketplace add SonarSource/sonarqube-agent-plugins\n/plugin install sonarqube@sonar /sonarqube:sonar-integrate Build trust into every line of code\n\nIntegrate SonarQube into your workflow and start finding vulnerabilities today.\n\n 4.6 / 5 Solutions Products Pricing Company Media Resources Compare Knowledge © 2026 SonarSource Sàrl. All rights reserved. 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