标签：#SEC

I keep finding the same Stripe webhook bugs in SaaS launches Most early SaaS billing bugs are not in Stripe Checkout itself. They are in the glue around it: trusting the success redirect instead of the signed webhook parsing JSON before signature verification missing idempotency for retry events reflecting verifier errors from unauthenticated webhook routes updating subscription state without a replay/audit trail letting "Pro" access drift from the payment source of truth Over the last few days I have been shipping small public fixes around exactly this class of problem. Recent examples: Morphix: Cloudflare Worker Stripe webhook with signature verification, Supabase subscription sync, and event idempotency ledger https://github.com/yiyuanlee/morphix/pull/25 Open Mercato: hardened unauthenticated payment/shipping provider webhooks against raw verifier error reflection and missing rate limiting https://github.com/open-mercato/open-mercato/pull/2680 Covenant: webhook signatures hardened against replay and secret rotation gaps https://github.com/wienerlabs/covenant/pull/229 Volunteerflow: made a Stripe invoice.paid Founders Circle counter update transactional instead of partially committing user/counter state https://github.com/ppppowers/volunteerflow-project/pull/49 The pattern is boring in the best possible way: payment systems should be boring. The 48-hour version For a small SaaS that is about to turn on paid plans, I can take a bounded payment assurance sprint: inspect Checkout / webhook / subscription state flow verify signed webhook handling and raw-body behavior add idempotency around Stripe retry events ensure subscription status and entitlement state have one source of truth add a small regression test or smoke script leave a deploy/runbook note so the next failure is diagnosable Fixed scopes I am taking: $2,000 / 48 hours: one payment path hardened and documented $5,000 / 5 days: full launch pass across Checkout, webhook, subscription mirror, Pro gate, pricin

South Korean authorities issued the record-breaking fine following a data breach that affected over 30 million customers.

Connecting to a payment gateway rarely fails because of business logic. More often, it fails at the very first technical step: authentication. If you’ve ever worked with payment APIs, you know the drill. Before sending a single request, you need to generate a cryptographic key pair and sign every request correctly. Sounds straightforward—until you actually try to do it. The hidden hurdle in every integration To securely call the Payneteasy API, each request must be signed. That means: Generating an RSA key pair (usually via OpenSSL) Converting between PKCS#1 and PKCS#8 formats Building a correct signature base string Percent-encoding everything properly Signing with RSA-SHA256 or HMAC-SHA1 Assembling the Authorization header One small mistake—a missing character, wrong encoding, or incorrect format—and your request gets rejected. For teams without deep cryptography expertise, this step alone can turn a one-day integration into a week-long debugging session. If you’re curious, the full manual process is documented here: https://doc.payneteasy.com/integration/general_api_usage/request_authentication_methods/oauth.html#generating-key-pair The solution: Key Pair Factory We built the Payneteasy Key Pair Factory to remove this bottleneck entirely. Instead of dealing with OpenSSL commands and key formats, you can generate everything you need in just a few clicks. What it does: Generates a ready-to-use RSA key pair Ensures correct formatting for Payneteasy APIs Eliminates manual conversion and configuration errors Keeps the private key on your side Provides a public key for request verification No cryptography expertise required. The tool is open-source and available on GitHub: https://github.com/payneteasy/key-pair-factory Why this matters This is not just about convenience—it directly impacts integration speed and success. With the Key Pair Factory, you get: Faster onboarding Fewer integration errors Less back-and-forth with support teams A smoother developer experience I

Cross-border purchasing platforms commonly use marketing tactics such as coupons, registration points, order rebates, and spend-based discounts to acquire new users and boost engagement. However, public promotions are prime targets for “wool hunters” (fraudsters) who exploit batch account registration, fake orders, malicious order spamming, and combined discount abuse to drain platform benefits, causing direct financial losses. Most purchasing systems lack dedicated event risk controls, making them highly vulnerable to batch exploitation as soon as a promotion goes live. This not only leads to financial losses but also crowds out genuine user benefits and distorts campaign effectiveness. This article details Taocarts’ comprehensive anti-fraud risk control framework, which uses multi‑dimensional behavior detection, rule‑based blocking, and account risk assessment to accurately distinguish real users from malicious exploiters, ensuring fair and controllable marketing activities. First, we identify common malicious exploitation scenarios and system vulnerabilities in cross‑border platforms: Batch account registration – Using new‑user exclusive coupons and registration points to harvest benefits repeatedly. Fake order placement and cancellation – Repeatedly claiming limited‑time discounts or rebates by placing and then canceling orders. Multiple accounts from the same device/IP – Spamming orders to consume activity quotas. Illegal discount stacking – Violating platform rules by combining multiple coupons or point deductions. Activity volume manipulation – Generating fake orders to earn activity rewards or points, creating false engagement data. Traditional systems have no risk rules and cannot detect batch operations or abnormal behavior, leading to wasted marketing spend and campaigns that actually lose money. Taocarts builds a fine‑grained anti‑exploit rule engine based on four dimensions: user behavior, device information, network characteristics, and order data, ena

The new open-source project could serve as the basis for a future of apps with features as complex as Slack, Discord, or Google Docs—but with added protection against surveillance.

Most people studying for Security+ use practice questions the wrong way. They take a 90 question set, score a 74, feel bad, take another set the next day, score a 76, and call that progress. Two weeks later the number has barely moved and they have no idea why. The score is the least useful thing a practice exam gives you. What you actually want is a map of what you do not know yet. Here is the approach that worked for getting through SY0-701 without burning out on endless question sets. Start cold, on purpose Before you study a single domain, take a full practice exam and do not look anything up. It will feel bad. That is the point. A cold score tells you where you actually stand, not where your notes say you should be. SY0-701 is split into five domains, and they are not weighted evenly: 1.0 General Security Concepts (12%) 2.0 Threats, Vulnerabilities, and Mitigations (22%) 3.0 Security Architecture (18%) 4.0 Security Operations (28%) 5.0 Security Program Management and Oversight (20%) Domain 4 alone is more than a quarter of the exam. If you bomb Security Operations and ace General Concepts, splitting your time evenly between them is a mistake. A cold diagnostic shows you that split in about an hour. If you want one to start with, there is a free diagnostic exam at secplusmastery.com/diagnostic that breaks your result down by domain so the holes are easy to see. Review the wrong answers, and the right ones too This single habit moved my scores more than anything else: for every question I missed, I wrote down why each wrong option was wrong, not just why the correct one was correct. Security+ loves distractors that are real terms used in the wrong context. A question about a control that prevents an attack will offer you a control that detects one, and a control that corrects after the fact, all as plausible answers. If you only learn that the answer was C, you learn nothing you can reuse. If you learn that B was a detective control and the scenario asked for a p

Audit thousands of passwords against data breaches — completely in your browser with zero-knowledge privacy. Published: June 9, 2026 TL;DR Most bulk password checkers require you to upload your entire vault. Utilora’s Bulk Password Breach Checker uses HIBP’s k-anonymity + local hashing so your passwords never leave your device . The Hidden Risk Most People Ignore Using a password manager is excellent, but it’s not enough. Many users unknowingly reuse or slightly modify passwords that have already been leaked in massive breaches (LinkedIn, Adobe, Yahoo, etc.). Manually checking hundreds or thousands of passwords is impractical — which is why people turn to bulk checkers. The problem? Most bulk checkers ask you to upload your password list . That creates a massive new privacy risk. How Utilora’s Zero-Knowledge Breach Checker Works We built this tool using a privacy-preserving technique called k-anonymity (popularized by Troy Hunt of Have I Been Pwned). Step-by-Step Technical Process: Local Hashing — Your browser uses the WebCrypto API to create a SHA-1 hash of each password locally. Prefix Only — Only the first 5 characters of the hash are sent to HIBP’s Range API. Server Response — HIBP returns hundreds of matching hashes that start with the same prefix. Local Comparison — Your browser checks if your full hash exists in the returned list. Result: HIBP knows someone checked a password starting with ABC12 , but has no idea which specific password it was. Why You Should Audit Your Entire Vault Regularly Discover weak or compromised passwords you forgot about Clean up old reused passwords Respond quickly after major breaches Maintain good password hygiene across all accounts Real-World Scenarios You exported your Bitwarden / 1Password / KeePass vault You want to check 500+ passwords before a security audit You just heard about a new major breach and want to verify impact You’re helping a family member or client secure their accounts How to Use the Tool Go to the Bulk Pas

zxcvbn is the most widely used password strength estimator with 1M npm downloads a week. It's also 389KB gzipped and hasn't shipped a commit since 2017. Most sign-up forms are hauling that around just to block password123 . Poor password UX is a real conversion problem. A strength meter that adds 389KB to your bundle delays page load — on mobile, measurably so. Users who hit a slow registration page don't wait. They leave. The irony is that most of that weight goes toward catching passwords nobody is actually using to register on your site. So I built passcore - 3.0KB gzipped and 98.4% detection rate on real breach data - same as zxcvbn, benchmarked against a deduped list of passwords pulled live from RockYou, Adobe, HIBP, and other major leak lists. zxcvbn takes ~9.7ms to load — it's parsing 389KB of dictionary into memory on every cold start. passcore loads in ~0.2ms. It evaluates a password in ~2,600 nanoseconds. For a registration form, it's effectively invisible — no jank, no layout shift, no contribution to your Core Web Vitals score. The strength meter shows up before the user finishes typing their first character. How it works: passcore runs five detection layers on every password: Dictionary - All entries sourced directly from breach data, not a generic word list Keyboard patterns - qwerty , asdf , 1234 , numpad walks Repeats - aaaa , ababab Sequences - abcdef , 123456 L33t speak - decodes p@ssw0rd → password , m0nk3y → monkey , then dictionary lookup The dictionary is small by design. Every entry was chosen because it appears in real breach data - not because it's a common English word. Password1! is caught not by a 40k word list but by stripping the suffix and checking if the core word is in the breach list. It is. The scoring model: passcore returns a score from 0 to 4 - same scale as zxcvbn. The detection layers run first. A dictionary match, keyboard pattern, repeat, sequence, or l33t substitution scores 0 or 1 immediately - no further calculation. If

Imagine you lose your work laptop on a commute. It holds 3 years of customer PII, internal product roadmaps, and access keys to your company's cloud infrastructure. Without full disk encryption enabled, anyone who finds the device can access every file in 10 minutes or less with a free bootable USB tool. With encryption enabled? They'll never access your data, even if they brute-force the password for decades. Per IBM's 2025 Cost of a Data Breach Report, organizations that use encryption save significantly on breach costs compared to teams that skip encryption. As cyber threats grow more sophisticated, and quantum computing edges closer to breaking legacy cryptographic standards, encryption is no longer an optional add-on—it's a core requirement for every digital system. This guide breaks down everything you need to know about data encryption, from core concepts to 2026's latest post-quantum developments, with actionable best practices for teams of all sizes. Table of Contents Core Concepts of Data Encryption How Does Data Encryption Work? Key Data Encryption Algorithms (2026 Approved & Deprecated) Encryption for All 3 Data States: At Rest, In Transit, In Use Real-World Data Encryption Use Cases Encryption Standards & Compliance Regulations Data Encryption Best Practices Common Encryption Mistakes to Avoid 2024-2026 Encryption Trends & Future Developments Conclusion & Key Takeaways References Core Concepts of Data Encryption Data encryption is a cryptographic process that converts human-readable plaintext into unreadable scrambled ciphertext using mathematical algorithms and secret keys. Only authorized parties with the correct decryption key can reverse the process to recover the original plaintext. Core Benefits of Encryption Encryption provides three non-negotiable security properties: Confidentiality : Only authorized users can access sensitive data Authentication : Verifies the origin of encrypted data Integrity : Confirms encrypted data has not been tampered w

The update brings a low-latency profile, speeds up search, and patches hundreds of flaws.

Typing a few letters and numbers into my web browser, I find myself gaping at the identity documents of complete strangers. The passport of a young woman from Germany. The passport of a man from Spain with glasses resting on his head. The front and back of another man's driver's license, a stereotypically goofy expression […]

Camouflage has always been graded by human eyes. But the thing hunting for you in 2026 is increasingly a detection model — so test against that. The premise Surveillance is automated now: drones, trail cameras, perimeter systems — most of what "sees" runs an object-detection network. Which makes traditional camouflage evaluation (a person squinting at a photo) the wrong test. The right test is adversarial: run the actual detector against your concealment and measure what it finds. That's the whole demo: upload a photo, and an object-detection model hunts for people in it — at four simulated distances — producing a detection-range profile and a stealth score. Simulating distance with pixels You can't move the camera after the photo is taken, but you can simulate the dominant factor in long-range detection: pixels on target . A person at 50 m simply occupies far fewer pixels than at 5 m. So each analysis run downscales the image progressively and re-runs detection: const DISTANCE_LEVELS = [ { label : ' Close (~5m) ' , scale : 1 }, { label : ' Mid (~15m) ' , scale : 0.45 }, { label : ' Far (~30m) ' , scale : 0.22 }, { label : ' Very far (~50m) ' , scale : 0.12 }, ]; for ( const level of DISTANCE_LEVELS ) { const scaled = drawScaled ( image , level . scale ); const detections = await model . detect ( scaled , 10 , 0.15 ); // best 'person' confidence at this simulated range } The output reads like a range card: detected at 5 m with 96% confidence, 41% at 15 m, invisible beyond 30 m. A stealth score aggregates it: how poorly did the adversary see you, averaged across ranges? Honest about the model The detector is COCO-SSD (a pretrained MobileNet-based model from the TensorFlow.js team) running entirely on-device — I didn't train it, and the demo says so on the page. The contribution here is the evaluation framework : using detectors as adversaries, simulating range, and turning subjective "good camo" into a measurable profile. The full version of this concept goes further

Introduction and Background The Rust ecosystem, celebrated for its memory safety and performance, relies heavily on crates —its package management system. These crates, hosted on the crates.io registry, are the building blocks of Rust projects, enabling developers to share and reuse code efficiently. However, this convenience comes with a hidden cost: a single compromised crate can cascade into a full-blown supply chain attack, as recently demonstrated by the 'onering' crate compromise . The Role of Crates in the Rust Ecosystem Crates serve as the backbone of Rust's dependency system. When a developer adds a crate to their project, Cargo , Rust's package manager, automatically resolves and includes all transitive dependencies. This mechanism, while streamlining development, amplifies the attack surface . A malicious crate, like 'onering', can propagate through multiple projects, executing harmful code during build or runtime. The attack on 'onering' exploited this very mechanism, leveraging the trust inherent in the Rust ecosystem to exfiltrate sensitive code from unsuspecting systems. The 'onering' Compromise: A Case Study in Supply Chain Vulnerability The 'onering' crate was compromised through a malicious code injection during an upload or update. This injection bypassed the insufficient security measures of the crates.io registry, which lacks robust checks for uploaded crates. Once deployed, the malicious code executed during the build process, exfiltrating sensitive data from systems that depended on it. The attack's success underscores the lack of developer vigilance regarding supply chain security and the overreliance on registry integrity . Systemic Failures and Their Mechanisms Insufficient Security Scanning: The crates.io registry failed to detect the malicious code during upload due to limited automated scanning capabilities . This allowed the compromised crate to enter the ecosystem undetected. Overreliance on Registry Security: Developers often trust cr

The ShinyHunters hacking gang claims to have compromised the Oracle PeopleSoft servers of more than 100 organizations, including many universities.

“Defenders cannot afford to take weeks to patch,” one Cybersecurity and Infrastructure Security Agency official warned on Wednesday.

US lawmakers are alarmed that Bill Pulte, a housing official with no intelligence experience, is poised to take charge of one of the government's most powerful surveillance tools.

North Korean hackers posing as remote IT workers and recruiters remain a major threat to U.S., European, and Asian companies, accounting for about half of all attacks over the past 12 months.

This is a follow-up to my earlier posts, and it is more of an open question than an answer. I have the data, I have a way to act, and I am genuinely unsure that acting is the right call. I could use the community's help thinking it through. Last week a supply-chain worm got into my GitHub account and repositories. I got out, cleaned up the proper way, and wrote it up. Then I checked the public list of repositories hit by the same worm, to see how the cleanup was going across the ecosystem. Nearly a week later, most of them are still carrying the live payload. It is worse than a count When you look closely, a lot of the owners are clearly trying. But they are missing how this actually works, in two ways that matter: Deleting is not removing. They remove the malicious files with an ordinary commit. That takes the payload off the branch tip, but the commit that introduced it is still in history, and the blob is still recoverable by anyone who reverts or checks out the old commit. The only real removal is rewriting history (reset, not revert) and asking GitHub to purge the objects, because the fork network keeps them reachable by SHA. One branch is not all branches. They clean the branch they know about and never see the backdated copies the worm planted on other branches, which are still live. And the part that genuinely worries me: some of these owners are almost certainly opening the infected repository in VS Code or an AI assistant to fix it , which is exactly the trigger that runs the payload again. The act of trying to clean it can re-detonate it. So: a large number of repositories still carrying a live credential stealer, and a large number of owners and contributors who do not know they are still exposed. The dilemma Here is where I am stuck. There are two paths and I do not like either. Report them to GitHub. Their response is automated and blunt. The repo gets disabled, with no human in the loop, the same hands-off automation that locked me out of my own accou

Cybersecurity researchers are complaining that Anthropic's new model Fable has guardrails that are too strict for any cybersecurity work.

Reentrancy is the oldest trick in smart contract exploitation. The DAO fell to it in 2016. You would think a bug this famous would be extinct by now. It isn't. Protocols still lose millions to reentrancy every year, because the textbook version is the easy one, and attackers stopped using the textbook version a long time ago. This post walks through the classic bug, then the modern variants that still bite in 2026: cross-function, cross-contract, read-only reentrancy through view functions, and callback reentrancy through ERC777 and ERC721. I'll show why ReentrancyGuard is not the silver bullet most developers think it is, and how an LLM-based auditor reasons about the call-then-state-change pattern in a way pattern matchers can't. The Classic: Single-Function Reentrancy Here is the canonical vulnerable withdraw. contract VulnerableVault { mapping(address => uint256) public balances; function deposit() external payable { balances[msg.sender] += msg.value; } function withdraw() external { uint256 amount = balances[msg.sender]; (bool success, ) = msg.sender.call{value: amount}(""); require(success, "transfer failed"); balances[msg.sender] = 0; // state update AFTER the external call } } The attack is simple. The attacker is a contract. When withdraw sends ETH via msg.sender.call , control passes to the attacker's receive() function before balances[msg.sender] is zeroed. The attacker calls withdraw again. The balance is still the original amount, so the vault pays out again. Repeat until the vault is empty. contract Attacker { VulnerableVault vault; receive() external payable { if (address(vault).balance >= 1 ether) { vault.withdraw(); // re-enter before balance is zeroed } } } The fix is checks-effects-interactions. Do all your checks, then update all your state, and only then make the external call. function withdraw() external { uint256 amount = balances[msg.sender]; balances[msg.sender] = 0; // effect first (bool success, ) = msg.sender.call{value: amount}(""); //