SShortSingh.
Back to feed

Hyperliquid Bridge Rated High Risk With $6.57B TVL at Stake, Audit Finds

0
·1 views

A security assessment dated September 3, 2026, evaluated the Hyperliquid Bridge, a cross-chain protocol holding approximately $6.57 billion in total value locked across Ethereum and several Layer 2 networks. The audit, conducted by a senior DeFi security researcher, assigned the bridge an overall risk score of 7.4 out of 10, classifying it as high risk. Critical concerns include a partially centralized validator set, where 4 of 11 guardian keys are controlled by a single entity, making coordinated key compromise feasible. Additional high-severity vulnerabilities were identified in the bridge's smart contracts, including a re-entrancy flaw and weak Merkle proof verification that could enable replay attacks. Medium-risk issues around unguarded proxy upgrades and reliance on a single oracle relayer further compound the protocol's exposure to sophisticated exploits.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

How JavaScript's Event Loop Handles Async Tasks While Staying Single-Threaded

JavaScript is single-threaded yet manages asynchronous operations like timers, API calls, and file reads without blocking execution — a capability made possible by the event loop. When async tasks are delegated to external APIs, their callbacks are queued and later picked up by the event loop to run on the call stack. The event loop relies on two distinct queues: the microtask queue, which handles Promise callbacks and runs at higher priority, and the macrotask queue, which handles setTimeout, I/O, and similar operations. Microtasks are always fully processed before the event loop moves on to any macrotask, which is why a resolved Promise callback executes before a setTimeout callback even if the timer appears earlier in the code. Grasping this execution order clarifies much of the seemingly unpredictable behaviour in asynchronous JavaScript programming.

0
ProgrammingDEV Community ·

SEO Strategy Must Now Cover AI Answers, Communities, and Traditional Search

A Search Engine Land analysis argues that SEO in 2027 extends well beyond Google rankings to include AI-generated answers, community platforms, and social channels. Tools like Google's AI Overviews, Gemini, and non-Google AI search are reshaping how users discover and evaluate brands before ever visiting a website. Platforms such as Reddit, forums, and video channels are also playing a growing role in building trust and influencing purchase decisions. Shopify data cited in the analysis shows AI-referred sessions rose notably year over year, with stronger initial conversions in some categories, signalling that discovery paths are diversifying. The analysis recommends that marketers plan content around where users begin their questions, ensuring it is credible and useful across all surfaces — not just optimised for keyword rankings.

0
ProgrammingDEV Community ·

Developer releases vue3-pdf-export to simplify PDF generation in Vue 3 apps

A developer has published vue3-pdf-export, an open-source Vue 3 and TypeScript package that generates PDFs directly from HTML in browser applications. The library was created after the author repeatedly rebuilt similar PDF components across multiple projects, including CRM and Google-related applications. It supports a wide range of features including pagination, headers, footers, watermarks, password protection, metadata, and compression. Built on top of existing tools like jsPDF, html2canvas, and html2pdf.js, the package adds a Vue-oriented layer so developers do not need to re-engineer common PDF requirements for each project. The library also provides progress reporting with named generation stages, making it easier to build responsive loading interfaces during PDF creation.

0
ProgrammingDEV Community ·

Blueprint Released for Building Enterprise-Grade MLOps Pipelines on AWS

A detailed architectural guide has been published outlining how to build a fully automated, production-grade MLOps pipeline using native AWS services. The blueprint addresses common production challenges such as data drift, training-serving mismatches, and risky manual rollbacks that arise without standardized workflows. The proposed architecture spans six functional layers, covering data ingestion, artifact versioning, pipeline orchestration, model governance, canary deployments, and continuous monitoring. Key AWS tools involved include SageMaker, Step Functions, EventBridge, CloudWatch, and API Gateway, among others. Security and compliance are central to the design, with all components operating inside private VPCs, TLS 1.3 encryption in transit, and AWS KMS-managed encryption at rest.

Hyperliquid Bridge Rated High Risk With $6.57B TVL at Stake, Audit Finds · ShortSingh