On December 3, 2025, the React team revealed a critical Remote Code Execution vulnerability in React Server Components (RSC). It affects several RSC packages and some of the most widely used React frameworks, including Next.js. A fix is already out, so the urgent step now is simply checking whether your project uses these packages—and updating to the patched versions if it does. Overview of the Vulnerability A newly reported flaw allows unauthenticated Remote Code Execution (RCE) on servers running React Server Components. Type: Unauthenticated Remote Code Execution CVE: CVE-2025-55182 (NIST , GitHub Advisory Database) Severity: CVSS 10.0 (Maximum severity) This means an attacker could execute arbitrary code on the server without any form of authentication, giving them full control of the affected environment. The issue is caused by a flaw in how React decodes payloads sent to React Server Function endpoints. A maliciously crafted HTTP request can trigger unsafe deserialization, leading to remote code execution. React will publish additional technical details once the patch rollout is fully completed. Scope of Impact Any application that supports React Server Components may be exposed, even if it never defines any Server Function endpoints. The vulnerability exists in the underlying RSC support layer used by multiple frameworks and bundlers. Your application is not vulnerable if: Your React code does not run on a server, or Your application does not use a framework, bundler, or plugin that supports React Server Components. Traditional client-only React applications are unaffected. Affected Versions and Components The vulnerability is tied to specific versions of the React Server Components packages and to the frameworks that depend on them. Identifying whether your project uses any of these versions is the first step in determining your exposure. Vulnerable Packages The issue affects the following packages in versions 19.0, 19.1.0, 19.1.1, and 19.2.0: react-server-dom-webpack react-server-dom-parcel react-server-dom-turbopack Affected Frameworks and Bundlers Several frameworks that rely on these packages are also impacted, including: Next.js React Router (when using unstable RSC APIs) Waku @parcel/rsc @vitejs/plugin-rsc Redwood SDK Security Fix and Recommended Actions The React team has released patched versions, and major frameworks have issued corresponding updates. Applying these fixes promptly is the only reliable way to remove the vulnerability from affected projects. Patched Versions The React team has released fixed versions: 19.0.1 19.1.2 19.2.1 (or any version newer than these) Upgrading to a patched release is mandatory to eliminate the vulnerability. Framework Updates Framework maintainers have also published security updates. For example, Next.js users must upgrade to one of the following patched versions: next@15.0.5 next@15.1.9 next@15.2.6 next@15.3.6 next@15.4.8 next@15.5.7 next@16.0.7 Other ecosystems (React Router, Redwood, Vite plugin, Parcel, Waku, etc.) also require upgrading to their latest patched versions. What Development Teams Should Do Now We recommend the following immediate steps: Audit all projects to confirm whether React Server Components or related frameworks are in use. Check package versions for the affected libraries listed above. Upgrade to the patched versions immediately if your application falls within the impacted scope. Review deployment environments for any unusual activity (optional but advisable for security). Document and report the findings to your internal security or project stakeholders. Conclusion This vulnerability (CVE-2025-55182) is one of the most severe vulnerabilities ever disclosed within the React ecosystem, and it may impact a wide range of modern React-based applications. To maintain security and prevent potential exploitation, all teams should: Review their applications, Identify affected components, and Apply the necessary upgrades without delay. If you need a security audit or patch support within your React-based web development projects, Haposoft is ready to step in.

Workloads spike, drop, and shift without warning, and fixed servers rarely keep up. AWS Lambda serverless architecture approaches this with a simple idea: run code only on events, scale instantly, and remove the burden of always-on infrastructure. It’s a model that reshapes how event-driven systems are designed and operated. Architecture of a Serverless System with AWS Lambda Event-driven systems depend on a few core pieces, and aws lambda serverless architecture keeps them tight and minimal. Everything starts with an event source, flows through a small, focused function, and ends in a downstream service that stores or distributes the result. Event Sources AWS Lambda is activated strictly by events. Typical sources include: S3 when an object is created or updated API Gateway for synchronous HTTP calls DynamoDB Streams for row-level changes SNS / SQS for asynchronous message handling Kinesis / EventBridge for high-volume or scheduled events CloudWatch Events for cron-based triggers Each trigger delivers structured context (request parameters, object keys, stream records, message payloads), allowing the function to determine the required operation without maintaining state between invocations. Lambda Function Layer Lambda functions are designed to remain small and focused. A function typically performs a single operation such as transformation, validation, computation, or routing. The architecture assumes: Stateless execution: no in-memory persistence between invocations. Externalized state: stored in services like S3, DynamoDB, Secrets Manager, or Parameter Store. Short execution cycles: predictable runtime and reduced cold-start sensitivity. Isolated environments: each invocation receives a dedicated runtime sandbox. This separation simplifies horizontal scaling and keeps failure domains small. Versioning and Aliases Lambda versioning provides immutable snapshots of function code and configuration. Once published, a version cannot be modified. Aliases act as pointers to specific versions (e.g., prod, staging, canary), enabling controlled traffic shifting. Typical scenarios include: Blue/Green Deployment: switch alias from version N → N+1 in one step. Canary Deployment: shift partial traffic to a new version. Rollback: repoint alias back to the previous version without redeploying code. This mechanism isolates code promotion from code packaging, making rollouts deterministic and reversible. Concurrency and Scaling Lambda scales by launching separate execution environments as event volume increases. AWS handles provisioning, lifecycle, and teardown automatically. Invocation-level guarantees ensure that scaling behavior aligns with event volume without manual intervention. Key controls include: Reserved Concurrency — caps the maximum number of parallel executions for a function to protect downstream systems (e.g., DynamoDB, RDS, third-party APIs). Provisioned Concurrency — keeps execution environments warm to minimize cold-start latency for latency-sensitive or high-traffic endpoints. Burst limits — define initial scaling throughput across regions. Reference Pipeline (S3 → Lambda → DynamoDB/SNS → Glacier) A common pattern in aws lambda serverless architecture is event-based data processing. This pipeline supports workloads such as media ingestion (VOD), IoT telemetry, log aggregation, ETL preprocessing, and other burst-driven data flows. Example flow: Integration Patterns in AWS Lambda Serverless Architecture Lambda typically works alongside other AWS services to support event-driven workloads. Most integrations fall into a few recurring patterns below. Lambda + S3 When new data lands in S3, Lambda doesn’t receive the file — it receives a compact event record that identifies what changed. Most of the logic starts by pulling the object or reading its metadata directly from the bucket. This integration is built around the idea that the arrival of data defines the start of the workflow. Typical operations Read the uploaded object Run validation or content checks Produce transformed or derivative outputs Store metadata or results in DynamoDB or another S3 prefix Lambda + DynamoDB Streams This integration behaves closer to a commit log than a file trigger. DynamoDB Streams guarantee ordered delivery per partition, and Lambda processes batches rather than single items. Failures reprocess the entire batch, so the function must be idempotent. Use cases tend to fall into a few categories: updating read models, syncing data to external services, publishing domain events, or capturing audit trails. The “before” and “after” images included in each record make it possible to detect exactly what changed without additional queries. Lambda + API Gateway Unlike S3 or Streams, the API Gateway path is synchronous. Lambda must complete within HTTP latency budgets and return a well-formed response. The function receives a full request context—headers, method, path parameters, JWT claims—and acts as the application logic behind the endpoint. A minimal handler usually: Validates the inbound request Executes domain logic Writes or reads from storage Returns JSON with proper status codes No queues, no retries, no batching—just request/response. This removes the need for EC2, load balancers, or container orchestration for API-level traffic. Lambda + Step Functions Here Lambda isn’t reacting to an event, it’s being invoked as part of a workflow. Step Functions control timing, retries, branching, and long-running coordination. Lambda performs whatever unit of work is assigned to that state, then hands the result back to the state machine. Workloads that fit this pattern: multi-stage data pipelines approval or review flows tasks that need controlled retries processes where orchestration is more important than compute Lambda + Messaging (SNS, SQS, EventBridge, Kinesis) Each messaging service integrates with Lambda differently: SNS delivers discrete messages for fan-out scenarios. One message → one invocation. SQS provides queue semantics; Lambda polls, receives batches, and must delete messages explicitly on success. EventBridge routes structured events based on rules and supports cross-account buses. Kinesis enforces shard-level ordering, and Lambda processes batches sequentially per shard. Depending on the source, the function may need to handle batching, ordering guarantees, partial retries, or DLQ routing. This category is the most varied because the semantics are completely different from one messaging service to another. Recommended Setup for AWS Lambda Serverless Architecture A practical baseline configuration that reflects typical usage patterns and cost behavior for a Lambda-based event-driven system. Technical Recommendations A stable Lambda-based architecture usually follows a small set of practical rules that keep execution predictable and operations lightweight: Function Structure Keep each Lambda focused on one task (SRP). Store configuration in environment variables for each environment (dev/staging/prod). Execution Controls Apply strict timeouts to prevent runaway compute and unnecessary billing. Enable retries for async triggers and route failed events to a DLQ (SQS or SNS). Security Assign least-privilege IAM roles so each function can access only what it actually needs. Observability Send logs to CloudWatch Logs. Use CloudWatch Metrics and X-Ray for tracing, latency analysis, and dependency visibility. Cost Profile and Expected Savings Below is a reference cost breakdown for a typical Lambda workload using the configuration above: Component Unit Price Usage Monthly Cost Lambda Invocations $0.20 / 1M 3M ~$0.60 Lambda Compute (512 MB, 200 ms) ~$0.0000008333 / ms ~600M ms ~$500 S3 Storage (with lifecycle) ~$0.023 / GB ~5 TB ~$115 Total – – ≈ $615/month With this model, teams typically see 40–60% lower cost compared to fixed server-based infrastructures, along with near-zero operational overhead because no servers need to be maintained or scaled manually. Cost Optimization Tips Lambda charges based on invocations + compute time, so smaller and shorter functions are naturally cheaper. Event-driven triggers ensure you pay only when real work happens. Apply multi-tier S3 storage: Standard → Standard-IA → Glacier depending on access frequency. Conclusion A serverless architecture aws lambda works best when the system is designed around clear execution paths and predictable event handling. With the right structure in place, the platform stays stable and cost-efficient even when workloads spike unexpectedly. Haposoft is an AWS consulting partner with hands-on experience delivering serverless systems using Lambda, API Gateway, S3, DynamoDB and Step Functions. We help teams review existing architectures, design new AWS workloads and optimize cloud cost without disrupting operations. If you need a practical, production-ready serverless architecture, Haposoft can support you from design to implementation.

When you submit an app to Google Play, most early failures surface in Closed Testing, not the final review. What we share here comes from real testing practice, and it’s what made handling those failures predictable for us. What Google Play Closed Testing Is Closed Testing is where Google first checks your app using real user activity, so it matters to understand what this stage actually requires. Where Closed Testing Fits in the Submission Process When you submit an app to Google Play, it doesn’t go straight to the final review. Before reaching that stage, every build must pass through Google’s internal testing tracks—Internal Testing → Closed Testing → Open Testing. Closed Testing sits in the middle of this flow and is the first point where Google expects real usage from real users. If the app fails here, it never reaches the actual “Submit for Review” step. That’s why many teams face repeated rejections without realizing the root cause comes from this stage, not the final review. Google Play Closed Testing in Simple Terms Google Play Closed Testing is a private release track where your app is shared with a small group of testers you select. These testers install the real build you intend to ship and use it in everyday conditions. The goal is straightforward: Google wants to see whether the app behaves like a complete product when real people interact with it. In this controlled environment, Google observes how users move through your features, how data is handled, and whether the experience matches what you describe in your Play Console settings. This is essentially Google’s early check to confirm that the app is stable, transparent, and built for genuine use—not just something assembled to pass review. What Google Expects During Closed Testing The core function of Google Play Closed Testing is to verify authenticity. Google wants evidence that your app is functional, transparent, and ready for real users, not a rushed build created solely to pass review. To make this evaluation, Google looks for a few key signals: Real testers using real, active Google accounts Real usage patterns, not one-off opens or artificial interactions Consistent engagement over time, typically around 14 days for most app types Actions inside your core features, not empty screens or placeholder flows Behavior that aligns with your Data Safety, privacy details, and feature declarations Evidence that the app is “alive”, such as logs, events, and navigation patterns generated from authentic interactions Google began tightening its review standards in 2023 after more unfinished and auto-generated apps started slipping into the submission flow. Instead of relying only on manual checks, Google now leans heavily on the activity recorded during Closed Testing to understand how an app performs under real use. This gives the review team a clearer picture of stability, data handling, and readiness—making Closed Testing a much more decisive step in whether an app moves forward. Why Google Play Closed Testing Is So Hard to Pass Most teams fail Closed Testing because their testing behavior doesn’t match the actual evaluation signals Google uses. The table below compares real developer mistakes with Google’s real criteria, so you can see exactly why each issue leads to rejection. Common Issues During Testing What Google Actually Checks Teams treat Closed Testing like internal QA. Testers only tap around the interface and rarely complete real user journeys. Google checks full, natural flows. It expects onboarding → core action → follow-up action. Shallow tapping does not confirm real functionality, so Google marks the test as lacking behavioral proof. Testers open the app once or twice and stop. Most activity happens on day 1, then engagement drops to zero. Google checks multi-day usage patterns. It needs recurring activity to evaluate stability and real adoption. One-off launches look like artificial or incomplete testing → fail. Core features remain untouched because testers don’t find or understand them. Navigation confusion prevents users from triggering important flows. Google checks whether declared core features are actually used. If users don’t naturally reach those flows, Google cannot validate them → flagged as “unverified behavior.” Permissions are declared but no tester enters flows that use them. e.g., camera, location, contacts, or other data-related actions never get triggered. Google cross-checks declared permissions with real behavior. If a permission never activates during testing, Google treats the Data Safety form as unverifiable → extremely high rejection rate. Engagement collapses after the first day. Testers lose interest quickly, resulting in long periods of zero activity. Google checks consistency over time (≈14 days). When usage dies early, the system sees weak, unreliable activity that does not resemble real-world usage → rejection. Passing Google Play Closed Testing: A Real Case Study Closed Testing turned out to be far stricter than we expected. What looked like a simple pre-release step quickly became the most decisive part of the review, and our team had to learn this the hard way—through three consecutive rejections before finally getting approved. The Three Issues That Held Us Back in Closed Testing These were the three recurring problems that blocked our app from moving past Google Play’s Closed Testing stage. #Issue 1: Having Testers, but Not Enough “Real” Activity In the first attempt, we only invited one person to join the test, so the app barely generated any meaningful activity. Most of the usage stopped at simple screen opens, and none of the core features were exercised in a way Google could evaluate. With such a small and shallow pattern, the system couldn't treat it as real user participation. The build was rejected right away for not meeting the minimum level of authentic activity. #Issue 2: Misunderstanding the “14-Day Activity” Requirement For the second round, we expanded the group to twelve testers, but most of them stopped using the app after just a few days. The remaining period showed almost no engagement, which meant the full 14-day window Google expects was never actually covered. Although the number of testers looked correct, the lack of continuous usage made the test inconclusive. Google dismissed the submission because the activity dropped off too early. #Issue 3: No Evidence of Real Activity (Logs, Tracking, or Records) By the third attempt, we finally kept twelve testers active for the entire duration, but we failed to capture what they did. There were no logs showing feature flows, no tracking to confirm event sequences, and no recordings for actions tied to sensitive permissions. From Google's viewpoint, the numbers in the dashboard had nothing to support them. Without verifiable evidence, the review team treated the activity as unreliable and rejected the build again. What Finally Helped Us Pass Google Play Closed Testing To fix the issues in the earlier attempts, the team reorganized the entire test instead of adding more testers at random. Everything was structured so Google could see consistent, authentic behaviour from real users. A larger tester group created a more reliable activity curve The previous rounds didn’t generate enough meaningful activity, so we increased the number of people involved. The larger group created a more natural engagement pattern that gave Google more complete usage signals to review. Extending the testing period from 14 to 17 consecutive days To avoid the early drop-off that hurt our earlier attempts, we kept the test running a little longer than the minimum 14 days. The longer duration prevented mid-test gaps and helped Google see continuous interaction across multiple days. Introducing a detailed daily checklist so testers covered the right flows Instead of letting everyone tap around freely, we provided a short list of the core actions Google needed to observe. A clear checklist guided testers through specific actions each day, producing consistent evidence for the features Google needed to verify. Enabling device-level tracking and full system logs Earlier data was too thin to validate behaviour, so we enabled device-level tracking and full system logs to review and later align with Google’s dashboard. This fixed the “invisible activity” issue from the earlier rounds and gave the review team something concrete to validate. Having testers record short videos of their actions Some flows involving permissions weren’t reflected clearly in logs, so testers recorded short clips when performing these tasks. These videos provided direct confirmation of how camera, file access and upload flows worked. Adding small features and content to encourage natural engagement The previous builds didn’t encourage repeated use, so we added minor features and content updates to create more realistic daily engagement. These adjustments helped testers interact with the app in a way that resembled real usage, not surface-level taps. Release Access Form: A Commonly Overlooked Step in the Approval Process After Closed Testing is completed, Google requires developers to submit the Release Access Form before the app can move forward in the publishing process. It sounds simple, but the way this form is written has a direct influence on the final review. Taking the form seriously, paired with the testing evidence we had already prepared, helped our final submission go through smoothly on the fourth attempt. Here’s what became clear when we worked through it: The answers must reflect the real behaviour of the app — especially the sections on intended use and where user data comes from. Any mismatch creates doubt. Google expects clear descriptions of features, user actions and the scope of testing. Vague explanations often slow the process down. Looking at how other developer communities handled this form helped us understand the phrasing that aligns with Google’s criteria. Final Thoughts Closed Testing is ultimately about proving that your app behaves like a real, ready-to-ship product. Most teams lose time because they only react after a rejection; we prevent 80% of those rejections long before you ship. If you want fewer surprises and a tighter, lower-risk review cycle, talk to us and Haposoft will run the entire review cycle for you.