If you want to age your sys admins 30 years overnight, remember that Active Directory is fully unicode compatible, so you can rename your laptop with emojis it its hostname, and it will reflect like that in AD
ping desktop-🤷♂️👍👌.mycompany.local
Announcing something new 👇
We’ve launched a Sentinel Cost Calculator to help you take control of SIEM spend.
✔ Predict costs upfront
✔ Build smarter security budgets
Take a look:
https://t.co/NodUn5f88c
#MicrosoftSentinel#Cybersecurity
😭
VS Code extensions are no different than browser extensions - high risk that you should be controlling with an allowlist
Yes, review and approval processes suck, but IR sucks even more
https://t.co/zsxhBhUsZN
The enterprise plans for Claude are cost prohibitive because you pay for API calls on top of flat billing per employee. Whereas the tier right below enterprise, Claude Team, includes a large bucket of token usage with each $20 dollar seat.
🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.
My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.
This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.
Hedgie🤗
And yet cloudflare makes a great point that everyone will soon be facing a pickle: patch fast and skip regression testing (causing bigger problems than the original vulnerability), OR, patch after 6(?) hours of regression testing then spend the rest of the weekend doing IR. 🤦♂️
For those of you just now paying attention to cybersecurity, large companies got hacked before AI.
Colonial Pipeline, SolarWinds, OPM, Kaseya, Aramco, Change Healthcare, Equifax, Target, Home Depot, TJX, etc
We are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such as our customers’ enterprises, organizations, and repositories), we are closely monitoring our infrastructure for follow-on activity.
Entra App Proxy continues to be one of the biggest hidden gems of Entra P1
For over a decade, we've been able to stop exposing risky apps to the Internet by routing through agents with outbound connections to Azure
I don't care what vendor you use, just get it off the Internet
“Patching faster does not change the shape of the pipeline that produces the patch. If regression testing takes a day, you cannot get to a two-hour SLA without skipping it, and the bugs you ship when you skip regression testing tend to be worse than the bugs you were trying to patch. We learned a version of this when we tried letting the model write its own patches and watched a few go out that fixed the original bug while quietly breaking something else the code depended on.
The harder question is what the architecture around the vulnerability should look like. The principle is to make exploitation harder for an attacker even when a bug exists, so that the gap between when a vulnerability is disclosed and when it is patched matters less. That means defenses that sit in front of the application and block the bug from being reached. It means designing the application so that a flaw in one part of the code cannot give an attacker access to other parts. It means being able to roll out a fix to every place the code is running at the same moment, rather than waiting on individual teams to deploy it. “
Cloudflare's security team spent the last few weeks testing Anthropic's Mythos against fifty of our own repositories. What we learned about offensive AI, why faster patching is the wrong reaction, and what the architecture around vulnerabilities has to look like next. https://t.co/RSrRtIhgaV
North Korean intelligence agents built an entire fake company to compromise one JavaScript developer. And it worked.
UNC1069 didn't hack Axios. They befriended its maintainer. They cloned a real company founder's identity, built a branded Slack workspace with fake employee profiles and LinkedIn post channels, then scheduled a Microsoft Teams call with what appeared to be a full team. During the call, a fake error message said his system needed an update. He installed it. That update was the RAT.
From one developer's laptop, they had everything: npm credentials, publishing access, the keys to a package installed in 80% of cloud environments. Axios gets 100 million downloads per week. The attackers published two poisoned versions at 12:21 AM UTC on a Sunday night, tagging both the latest and legacy branches within 39 minutes. The malicious dependency had been pre-staged 18 hours earlier with a clean decoy version to build registry history. Three separate RAT payloads were pre-built for macOS, Windows, and Linux. The malware self-deleted after execution to erase forensic evidence.
The poisoned versions were live for about three hours before npm pulled them. Huntress observed 135 endpoints across all operating systems calling the attacker's command-and-control server during that window. Wiz found the malicious versions in roughly 3% of environments scanned. Every affected machine needs full credential rotation: npm tokens, AWS keys, SSH keys, CI/CD secrets, everything in .env files.
The part that keeps getting worse: this isn't isolated. The same threat cluster compromised Trivy (a security scanner), KICS, LiteLLM, and multiple GitHub Actions in the two weeks before Axios. Google estimates hundreds of thousands of stolen secrets are now circulating from these combined attacks. The maintainer had 2FA enabled. He said himself: "I have 2FA/MFA on practically everything." The exact method of token compromise is still undetermined.
One person. One fake Teams call. 100 million weekly downloads weaponized in under three hours. The npm ecosystem runs on mass trust in individual maintainers who volunteer their time, and North Korean intelligence now has a repeatable playbook for turning that trust into a delivery mechanism.
The entire Axios meltdown was social engineering to get a dev to join a fake Microsoft Teams call and the dev trying to update Microsoft Teams.
Incredible.
Someone just poisoned the Python package that manages AI API keys for NASA, Netflix, Stripe, and NVIDIA.. 97 million downloads a month.. and a simple pip install was enough to steal everything on your machine.
The attacker picked the one package whose entire job is holding every AI credential in the organization in one place. OpenAI keys, Anthropic keys, Google keys, Amazon keys… all routed through one proxy. All compromised at once.
The poisoned version was published straight to PyPI.. no code on GitHub.. no release tag.. no review. Just a file that Python runs automatically on startup. You didn’t need to import it. You didn’t need to call it. The malware fired the second the package existed on your machine.
The attacker vibe coded it… the malware was so sloppy it crashed computers.. used so much RAM a developer noticed their machine dying and investigated. They found LiteLLM had been pulled in through a Cursor MCP plugin they didn’t even know they had.
That crash is the only reason thousands of companies aren’t fully exfiltrated right now. If the code had been cleaner nobody notices for weeks. Maybe months.
The attack chain is the part that gets worse every sentence.
TeamPCP compromised Trivy first. A security scanning tool. On March 19. LiteLLM used Trivy in its own CI pipeline… so the credentials stolen from the SECURITY product were used to hijack the AI product that holds all your other credentials.
Then they hit GitHub Actions. Then Docker Hub. Then npm. Then Open VSX. Five package ecosystems in two weeks. Each breach giving them the credentials to unlock the next one.
The payload was three stages.. harvest every SSH key, cloud token, Kubernetes secret, crypto wallet, and .env file on the machine.. deploy privileged containers across every node in the cluster.. install a persistent backdoor waiting for new instructions.
TeamPCP posted on Telegram after: “Many of your favourite security tools and open-source projects will be targeted in the months to come.. stay tuned.”
Every AI agent, copilot, and internal tool your company shipped this year runs on hundreds of packages exactly like this one… nobody chose to install LiteLLM on that developer’s machine. It came in as a dependency of a dependency of a plugin. One compromised maintainer account turned the entire trust chain into a credential harvesting operation across thousands of production environments in hours.
The companies deploying AI the fastest right now have the least visibility into what’s underneath it.
A company that sells cybersecurity risk intelligence to 91% of Fortune 100 companies just got breached through an unpatched React app and a single overprivileged AWS role.
LexisNexis. 3.9 million records. 400,000 user profiles. 53 secrets extracted in plaintext from AWS Secrets Manager. Including credentials for production databases, Salesforce, Oracle, and analytics platforms.
The password "Lexis1234" was reused across five different internal systems.
This is a company that describes itself as "one of the largest protectors of private and confidential data in the world." They provide risk intelligence to 7,500 US government agencies, nine out of ten banks, and major insurers globally. They sell cybersecurity assessments to their customers.
And they couldn't secure their own AWS account.
Here's what makes this worse than a typical breach:
- The compromised data includes accounts tied to 118 .gov email domains. Three US federal judges. Four Department of Justice attorneys. SEC staff. Probation officers. Federal court law clerks. The attackers published doxxed profiles of federal officials tied to courts and regulatory agencies across the country.
- These aren't random consumer records. These are the digital identities of people whose exposure carries national security implications. A compromised federal judge's profile doesn't just enable identity theft - it enables targeted influence operations, blackmail, and intelligence gathering.
The attack path is textbook and that's the problem:
→ Unpatched React application - the front door
→ Single ECS task role with read access to every secret in the account - the keys to everything
→ 536 Redshift tables, 430+ database tables, full VPC infrastructure mapping - complete visibility
→ 53 secrets in plaintext including database credentials, API tokens, and development access keys
No zero-day. No advanced persistent threat. No nation-state capability required. Basic hygiene failures — unpatched app, overprivileged IAM role, password reuse, plaintext secrets.
This is LexisNexis's second confirmed breach in two years. The December 2024 incident exposed 364,000 individuals through a compromised corporate account on a third-party development platform.
Data brokers and analytics providers are not peripheral players - they're deeply embedded in today's risk landscape.
That's the pattern we keep seeing. Attack the aggregator, not the individual. BPO providers. Cloud platforms. Legal data giants. The organisations that hold everyone else's data are the highest-value targets - and often the weakest links.
For every enterprise that uses LexisNexis services:
→ Assume your metadata, contract details, and product usage history are exposed → Watch for targeted phishing using the exposed business relationship data
→ If your staff have LexisNexis accounts, reset credentials immediately
→ Ask your vendor risk team: when was the last time we assessed LexisNexis's actual security posture - not their marketing, their controls?
The company that indexes the world's legal information couldn't index its own IAM policies. And they're not the exception. They're the pattern.
More info: https://t.co/lzgKNNraWf
Microsoft’s June 2026 Secure Boot certificate update is not a routine patch—it’s a foundational platform trust change that requires deliberate planning. In this session, we’ll break down what Secure Boot is, why the upcoming certificate update matters to your Windows estate, and how to use Microsoft Intune to inventory, assess readiness, and automate deployment at scale. You’ll leave with a practical roadmap to reduce risk and avoid disruption.
🚨 Secure Boot 2026 is coming—and it’s not just another patch.
Microsoft’s June certificate update impacts platform trust. Are your Windows endpoints ready?
Join this technical session to learn how to assess & prepare with Intune.
Register: https://t.co/fN3ikpDnnn
#secureboot