SShortSingh.
Back to feed

Independent Audit Flags Medium-High Risk in MEXC Cross-Chain Bridge Worth $5.5B

0
·3 views

An independent DeFi security researcher published a risk assessment on September 25, 2026, evaluating MEXC's cross-chain bridge, which holds approximately $5.5 billion in total value locked across Ethereum, multiple Layer-2 networks, and non-EVM chains. The bridge received a composite risk score of 6.2 out of 10, with the lowest ratings in oracle and relayer security and governance upgradeability. Auditors identified a critical re-entrancy vulnerability in the BridgeRelayer contract that could allow unlimited minting of wrapped assets, potentially draining the entire TVL. Additional concerns include a small 12-node validator set vulnerable to collusion, single-key relayer signing susceptible to forgery, and an upgradeable proxy controlled by a single multisig address with no timelock. The report calls for immediate remediation of smart-contract bugs and hardening of relayer infrastructure to reduce the overall risk profile.

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 to Fix Python PDF Preview Timeouts by Managing Pages, DPI, and Render Budgets

A technical guide published on DEV Community explains how to debug PDF-to-image conversion timeouts in Python by treating the problem as a render-budget issue rather than a simple timeout configuration. Developers are advised to measure page count, pixel dimensions, and per-page rendering time before adjusting any timeout settings. The article notes that file size is a poor proxy for rendering complexity, since a single-page PDF with a large embedded image can be far more demanding than an 80-page text document. A sample Python implementation demonstrates how to cap page counts and separate fast interactive previews from slower asynchronous batch rendering. The guide recommends using two distinct performance budgets — one for the first visible page and another for remaining pages — to keep preview features predictable under varying document loads.

0
ProgrammingDEV Community ·

What Actually Happens Between Typing a URL and a Webpage Loading

When a user types a URL and presses Enter, the browser first parses the address into its components — protocol, domain, and path — before initiating any network activity. It then checks multiple cache layers to determine whether stored data can be reused, potentially speeding up the process. If a network request is needed, DNS translates the human-readable domain name into a numeric IP address so the browser knows where to connect. For secure HTTPS connections, a TLS handshake is performed to establish encryption, authentication, and data integrity before any content is exchanged. Finally, the server responds with HTML, CSS, and JavaScript resources, which the browser processes and renders into the visible webpage.

0
ProgrammingDEV Community ·

Claude AI Identifies Novel Enzyme System in Bacteriophage DNA Called ART

Anthropic's biology research lab announced on September 23 that its Claude AI identified a previously undescribed enzyme system in bacteriophage DNA, dubbed array-associated reverse transcriptases, or ART. Around 950 Claude agents processed over 210 million tokens across 21 hours, systematically narrowing more than 200,000 reverse transcriptase candidates down to 20 before flagging an unusual repeating DNA pattern for human review. The discovery is not of a new molecule but of a novel relationship between already-known components — a reverse transcriptase, a neighboring gene, and a repeat array — functioning together as a single system. ART structurally resembles CRISPR arrays and may be programmable for DNA operations, though Anthropic acknowledges its function remains unknown. The findings exist only as a preprint pending peer review, and all physical laboratory experiments were conducted by human scientists.

0
ProgrammingDEV Community ·

Dev builds AI fraud investigation agent using TigerGraph and multi-agent reasoning

Developer Vansh Deo of team QueryCrew built Zyg0s, an autonomous fraud investigation platform, as part of the TigerGraph Agentic Fraud Investigation Hackathon held under the Hacker House Goa track. The system uses TigerGraph Savanna Cloud, Model Context Protocol, and Groq to power seven specialized AI agents that analyze over 590,000 financial transactions from the IEEE-CIS dataset. Rather than relying on simple binary fraud classification, Zyg0s models investigations as an eight-stage evidentiary process, grading evidence across a four-tier defensibility framework and quantifying uncertainty. The platform enforces bank fraud policy rules, generates regulator-grade FinCEN BSA/AML Suspicious Activity Reports, and stores case outcomes in graph-native memory. It was designed to address key industry pain points including high false-alarm rates, risks of wrongly freezing customer accounts, and the inability of generic LLMs to produce legally defensible audit trails.