#NodeGuard
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*Please use only for services you own and manage directly.

If you need to inspect internal network services or deployment/worker environments that cannot be accessed from outside, please leave a message on the NodeGuard page.

#Nodejs #Node2Shell #CVE2026111497 #NodeGuard #Xint
October 26, 2026 at 8:55 PM
xint-io.bsky.social
🔥 Node2Shell vulnerability scanner 'NodeGuard' unveiled

We are releasing NodeGuard, a free inspection service for Node2Shell (CVE-2026-111497).
Enter the service URL to quickly check the likelihood of exposure and immediately see the results and recommended actions.

👉 nodejsguard.io
Node2Shell Scanner - Powered by Xint
nodejsguard.io
October 26, 2026 at 8:55 PM
xint-io.bsky.social
📌 How to use NodeGuard
• Enter the URL to be inspected, check the assessment results and recommended actions displayed on the screen
• Domains, subdomains: If there are multiple API endpoints, check each one
• Check again after patching using the same URL to see if the vulnerability was addressed
October 26, 2026 at 8:55 PM
users.rust-lang.org.web.brid.gy
Is it a safe implementation of mutable iterator for a tree?
You don’t need `unsafe` here. Put the `node` in an `Option`, so that you can `take()` it when it is time to access its children, and get references with the `'a` lifetime: struct NodeGuard<'a: 'b, 'b, T> { iter: &'b mut NodeMutIter<'a, T>, node: Option<&'a mut Node<T>>, } impl<'a, 'b, T> Drop for NodeGuard<'a, 'b, T> { fn drop(&mut self) { for n in self.node.take().unwrap().children.iter_mut().rev() { // ^^^^^^^^^^^^^^^^ self.iter.nodes.push_front(n); } } } impl<'a, 'b, T> Deref for NodeGuard<'a, 'b, T> { type Target = Node<T>; fn deref(&self) -> &Self::Target { self.node.as_ref().unwrap() } } impl<'a, 'b, T> DerefMut for NodeGuard<'a, 'b, T> { fn deref_mut(&mut self) -> &mut Self::Target { self.node.as_mut().unwrap() } } This is essentially a workaround for the fact that `Drop::drop()` is given a mutable reference rather than ownership of the fields.
users.rust-lang.org
March 1, 2025 at 8:31 AM
eprint.ing.bot
NodeGuard: A Highly Efficient Two-Party Computation Framework for Training Large-Scale Gradient Boosting Decision Tree (Tianxiang Dai, Yufan Jiang, Yong Li, Fei Mei) ia.cr/2024/535
Abstract. The Gradient Boosting Decision Tree (GBDT) is a well-known machine learning algorithm, which achieves high performance and outstanding interpretability in real-world scenes such as fraud detection, online marketing and risk management. Meanwhile, two data owners can jointly train a GBDT model without disclosing their private dataset by executing secure Multi-Party Computation (MPC) protocols. In this work, we propose NodeGuard, a highly efficient two party computation (2PC) framework for large-scale GBDT training and inference. NodeGuard guarantees that no sensitive intermediate results are leaked in the training and inference. The efficiency advantage of NodeGuard is achieved by applying a novel keyed bucket aggregation protocol, which optimizes the communication and computation complexity globally in the training. Additionally, we introduce a probabilistic approximate division protocol with an optimization for re-scaling, when the divisor is publicly known. Finally, we compare NodeGuard to state-of-the-art frameworks, and we show that NodeGuard is extremely efficient. It can improve the privacy preserving GBDT training performance by a factor of 5.0 to 131 in LAN and 2.7 to 457 in WAN. Image showing part 2 of abstract.
April 6, 2024 at 4:20 AM