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Category Archives: Cybersecurity

For the second time in a row, a post by cr0w on Mastodon regarding the Chrome release blog appearing to not render anything resulted in me firing up lynx to show a sub-second load and render, then finally doing something a bit more tangible about the situation.

The 81-Second Wall

The Google Blogger pages load an ancient copy of jQuery (1.11.3, from 2015) synchronously in the <head>, alongside a 53KB widgets.js Blogger framework. Then, posts like this one — https://chromereleases.googleblog.com/2026/06/stable-channel-update-for-desktop_0175352312.html — stuff 433 CVE entries into the DOM — 670KB of HTML, 5,045 nodes. The Blogger WidgetManager processes all of that against the DOM using jQuery’s notoriously slow selector engine, and the main thread stays locked for 81 seconds. Nothing else runs. Not even the HTTP request for the DoubleClick tracking pixel queued behind it (because ofc there’s a DoubleClick tracking pixel).

The Safari Navigation Timing API numbers make it embarrassingly concrete:

  • responseEnd: 143ms
  • domInteractive: 231ms
  • domContentLoaded: 81,280ms

That’s 81 seconds between “DOM is ready” and “page is loaded.” All burning prescious CPU cycles with zero network activity during that window.

Why This Matters (Again)

This is the second time I’ve felt compelled to dig into this particular mess. The Chrome Releases page is a real/tangible operational resource — security teams, vulnerability managers, and researchers (somewhat, at least) depend on it for CVE data. When it’s broken, it creates a bottleneck for people who have real jobs to do.

The 433 CVE entries choking the page are exactly what people came to read. But they’re baked into the HTML as rendered text, not exposed as structured data anywhere. So even when the page eventually loads, you’re still scraping HTML to get at anything useful.

unjam

unjam solves that problem. It’s a small CLI that connects to a Blogger page and extracts structured data — both the widget configuration from the _WidgetManager._SetDataContext inline script and the CVE entries from Chrome Release posts — without touching a browser at all.

It’s a single Deno binary for macOS, Linux, and Windows. No dependencies, no configuration overhead, just download and run:

unjam --cve https://chromereleases.googleblog.com/2026/06/stable-channel-update-for-desktop_0175352312.html
[
  {
    "issueId": "506558270",
    "issueUrl": "https://issues.chromium.org/issues/506558270",
    "severity": "Critical",
    "cveId": "CVE-2026-13774",
    "description": "Use after free in Extensions.",
    "reporter": "Google",
    "reportedOn": "2026-04-26"
  },
  {
    "issueId": "511766407",
    "issueUrl": "https://issues.chromium.org/issues/511766407",
    "severity": "Critical",
    "cveId": "CVE-2026-13775",
    "description": "Use after free in GPU.",
    "reporter": "Google",
    "reportedOn": "2026-05-10"
  },
  …
]

Getting CVE data from one of these posts used to mean waiting 81 seconds for a browser tab to finish wrestling with jQuery, then hand-scraping HTML. Now it takes about a second and returns clean JSON. The --cve flag parses each entry into structured fields — CVE ID, severity, description, issue tracker URL, reporter, and date reported — ready to pipe into jq, load into a database, or feed into whatever vulnerability management pipeline you’re running.

The tool also handles the general case: any Blogger page carrying the _WidgetManager._SetDataContext inline script can be unwedged with the default mode, which converts the JavaScript object literal into proper JSON. That turned out to be useful enough to bake in as default functionality.

The project’s at https://git.sr.ht/~hrbrmstr/unjam and has pre-built binaries for popular platforms.

I don’t expect this page to stay broken forever…I mean, someone at Google will eventually update the template (right, Anakin? right? Anakin?), and may even quietly drop the DoubleClick pixel (LOL) — but until then, unjam fills the gap cleanly.

Cloudflare announced PACT this week — privacy-preserving tokens to separate humans from bots, backed by Google, Mozilla, Microsoft, and Shopify. The cryptography is solid. The governance model doesn’t exist yet.

I wrote about why the “ratchet effect” should worry anyone who cares about the open web, how this is Web Environment Integrity re-skinned for the third time, and who gets left behind when tokens become the default access pass.

Read the full post.

May 2026 dropped three critical Linux vulnerabilities on a near-weekly cadence, and the security discourse has mostly treated them as three separate bad days. They’re not. Together they form a reliable, race-free, forensically quiet kill chain from the public internet to root, and if you’re running nginx in front of anything that matters, you need to stop and read this.

CVE-2026-42945, dubbed NGINX Rift, landed May 13 courtesy of depthfirst. It’s a heap buffer overflow in ngx_http_rewrite_module that’s been sitting in every nginx build since 2008. An unauthenticated attacker sends a single crafted HTTP request and overwrites the heap, getting remote code execution in the worker process – no auth, no prior session, no prerequisites beyond a network path to port 80 or 443. The root cause is a mismatch between two passes over the rewrite directives: the length calculation runs with is_args=0 (raw byte count) while the copy pass runs with is_args=1 (URI-escaped), so the write overruns the allocation. The trigger is a configuration pattern that’s everywhere: a rewrite directive with an unnamed PCRE capture ($1, $2) and a question mark in the replacement string, followed by another rewrite, if, or set in the same block. CVSS 9.2, and it earns it.

CVE-2026-31431, “Copy Fail,” came from Theori on April 29. It’s a logic bug in the authencesn cryptographic template that lets an unprivileged local user write 4 controlled bytes into the page cache of any readable file, then pivot to root. The exploit is 732 bytes of Python (no races, no disk writes, no forensic residue – the page cache corruption means file integrity checks pass because the underlying file on disk was never touched). It works on every distro shipped since 2017. CISA added it to the Known Exploited Vulnerabilities catalog with a May 15 remediation deadline.

Then there’s CVE-2026-43284 and CVE-2026-43500, “Dirty Frag,” disclosed May 7 by Hyunwoo Kim. It’s a two-bug chain that lands in the same place as Copy Fail – page-cache-to-root LPE – but routes around the Copy Fail mitigation entirely. If you blacklisted algif_aead thinking you were covered, Dirty Frag gets there through xfrm-ESP or rxrpc instead. Microsoft’s already seeing in-the-wild activity: SSH foothold, stage an ELF binary, escalate via su. Deterministic. No races. Same bug class, different sink.

Why does the combination matter more than any single bug? Exploit chains are usually academic exercises, published to demonstrate feasibility and then left to rot in a CTF writeup. This isn’t that. CVE-2026-42945 hands you a foothold from the internet. CVE-2026-31431 or CVE-2026-43284 hands you root once you’re on the box. Neither step requires races, user interaction, or authentication. Neither leaves obvious forensic traces on disk. Both have working, published proof-of-concept code as of this writing.

The surface area here is genuinely uncomfortable. NGINX is the most-deployed web server on the planet. WordPress – with scads of massively-deployed plugins recommended NGINX configuration contains the exact vulnerable rewrite pattern (I checked; it’s right there in the docs) – powers something north of 40% of the web. That means whitehouse.gov, NASA, the UK Government, the Australian Government, the State of California, and essentially every major US university is potentially in scope. Every federal agency required by the 21st Century IDEA Act to maintain a public web presence. Every municipality running WordPress on a LEMP stack. Every SaaS app behind an NGINX ingress controller. An attacker doesn’t need a zero-day chain for any of these; they need access to data from a public internet scanner, a grep for vulnerable version strings, and the ability to send one HTTP request.

I shipped a static configuration scanner for the NGINX Rift pattern. Single bash script, no dependencies beyond bash 4+ and grep, runs offline against config files without touching a live nginx process:

git clone https://git.sr.ht/~hrbrmstr/cve-2026-42945-scanner
cd cve-2026-42945-scanner
./scan-nginx-rift.sh /etc/nginx

Run it on every box running nginx. Add --json in CI. Point it at ingress controller configmaps. The output tells you the file, the line number, the vulnerable directive, and which following directive creates the exploitability condition:

[VULN] sites-enabled/wordpress.conf:8 – rewrite ^/([^/]+?)-sitemap([0-9]+)?.xml$
          followed by "if" at line 12

If you find a hit, you’ve got two options in order of preference:

  1. Upgrade nginx to 1.30.1 (stable) or 1.31.0 (mainline).
  2. Replace unnamed captures with named captures in every affected rewrite:
# Before (vulnerable)
rewrite ^/([^/]+?)-sitemap([0-9]+)?.xml$ /index.php?sitemap=$1&sitemap_n=$2 last;

# After (safe)
rewrite ^/(?<term>[^/]+?)-sitemap(?<num>[0-9]+)?.xml$ /index.php?sitemap=$term&sitemap_n=$num last;

For the kernel side, check your distro’s patch status now and don’t trust “we’ll get to it.” If you can’t patch immediately, blacklisting algif_aead blocks Copy Fail but does nothing for Dirty Frag. For Dirty Frag, unload xfrm_algo.ko and rxrpc.ko if your workload doesn’t need them, and make sure AppArmor or SELinux policy is blocking unprivileged user namespaces.

Three critical Linux CVEs in three weeks, all with published exploits, all in code that’s been shipping for years. The gap between disclosure and working exploit is now measured in hours, not months. The scanner above closes one piece of that gap for the nginx side. The rest depends on whether you check your configs today or wait until something in your logs looks wrong – at which point the forensic-residue-free LPE means “looking wrong” may be all you ever see.

At our previous employer, the global deception and detection infrastructure generates tons of events that eventually make their way into an ever-growing data lake with (as of February 2026) 22 TB of PCAPs and 32 TB of session protocol data. When trying to find novel and truly dangerous attacker behavior, the bottleneck isn’t the data — it’s the analyst trying to hold it all in their head while toggling between Arkime, Censys, VirusTotal, and five other tabs.

Glenn Thorpe and I built Orbie to attack that problem. It’s a prompt-engineered analytical system running in Claude Code that coordinates 16 data source integrations, 8 investigation skills, and 2 background enrichment agents across structured, reproducible workflows — with one rule we never bent on: never assume, always query, show your work.

The full architecture, the failure modes, and where it’s going are in the talk we gave at the February 2026 installment of [un]prompted, above, and you can get some more info and freebies at https://github.com/GreyNoise-Intelligence/2026-labs-unprompted.

There’s going to be another [un]prompted likely later this year and I highly recommend attending and — if you have some of your own accomplishments to share — presenting. It was an incredible experience.

In the past ~4 weeks I have personally observed some irrefutable things in “AI” that are very likely going to cause massive shocks to employment models in IT, software development, systems administration, and cybersecurity. I know some have already seen minor shocks. They are nothing compared to what’s highly probably ahead.

Nobody likely wants to hear this, but you absolutely need to make or take time this year to identify what you can do that AI cannot do and create some of those items if your list is short or empty.

The weavers in the 1800s used violence to get a 20-year pseudo-reprieve before they were pushed into obsolescence. We’ve got ~maybe 18 months. I’m as pushback-on-this-“AI”-thing as makes sense. I’d like for the bubble to burst. Even if it does, the rulers of our clicktatorship will just fuel a quick rebuild.

Four human-only capabilities in security

In my (broad) field, I think there are some things that make humans 110% necessary. Here’s my list — and it’d be great if folks in very subdomain-specific parts of cyber would provide similar ones. I try to stay in my lane.

1. Judgment under uncertainty with real consequences

These new “AI” systems can use tools to analyze a gazillion sessions and cluster payloads, but they do not (or absolutely should not) bear responsibility for the “we’re pulling the plug on production” decision at 3am. This “weight of consequence” shapes human expertise in ways that inform intuition, risk tolerance, and the ability to act decisively with incomplete information.

Organizations will continue needing people who can own outcomes, not just produce analysis.

2. Adversarial creativity and novel problem framing

The more recent “AI” systems are actually darn good at pattern matching against known patterns and recombining existing approaches. They absolutely suck at the “genuinely novel” — the attack vector nobody has documented, the defensive technique that requires understanding how a specific organization actually operates versus how it should operate.

The best security practitioners think like attackers in ways that go beyond “here are common TTPs.”

3. Institutional knowledge and relationship capital

A yuge one.

Understanding that the finance team always ignores security warnings — especially Dave — during quarter-close. That the legacy SCADA system can’t be patched because the vendor went bankrupt in 2019. That the CISO and CTO have a long-running disagreement about cloud migration.

This context shapes what recommendations are actually actionable. Many technically correct analyses are organizationally useless.

4. The ability to build and maintain trust

The biggest one.

When a breach happens, executives don’t want a report from an “AI”. They want someone who can look them in the eye, explain what happened, and take ownership of the path forward. The human element of security leadership is absolutely not going away.

How to develop these capabilities

Develop depth in areas that require your presence or legal accountability. Disciplines such as incident response, compliance attestation, or security architecture for air-gapped or classified environments. These have regulatory and practical barriers to full automation.

Build expertise in the seams between systems. Understanding how a given combination of legacy mainframe, cloud services, and OT environment actually interconnects requires the kind of institutional archaeology (or the powers of a sexton) that doesn’t exist in training data.

Get comfortable being the human in the loop. I know this will get me tapping mute or block a lot, but you’re going to need to get comfortable being the human in the loop for “AI”-augmented workflows. The analyst who can effectively direct tools, validate outputs (b/c these things will always make stuff up), and translate findings for different audiences has a different job than before but still a necessary one.

Learn to ask better questions. Bring your hypotheses, domain expertise, and knowing which threads are worth pulling to the table. That editorial judgment about what matters is undervalued, and is going to take a while to infuse into “AI” systems.

We’re all John Henry now

A year ago, even with long covid brain fog, I could out-“John Henry” all of the commercial AI models at programming, cyber, and writing tasks. Both in speed and quality.

Now, with the fog gone, I’m likely ~3 months away from being slower than “AI” on a substantial number of core tasks that it can absolutely do. I’ve seen it. I’ve validated the outputs. It sucks. It really really sucks. And it’s not because I’m feeble or have some other undisclosed brain condition (unlike 47). These systems are being curated to do exactly that: erase all of us John Henrys.

The folks who thrive will be those who can figure out what “AI” capabilities aren’t complete garbage and wield them with uniquely human judgment rather than competing on tasks where “AI” has clear advantages.

The pipeline problem

The very uncomfortable truth: there will be fewer entry-level positions that consist primarily of “look at alerts and escalate.” That pipeline into the field is narrowing at a frightening pace.

What concerns me most isn’t the senior practitioners. We’ll adapt and likely become that much more effective. It’s the junior folks who won’t get the years of pattern exposure that built our intuition in the first place.

That’s a pipeline problem the industry hasn’t seriously grappled with yet — and isn’t likely to b/c of the hot, thin air in the offices and boardrooms of myopic and greedy senior executives.

(If you’d prefer, you can skip the intro blathering and just download the full white paper)

Back in 1997, a commercial airline captain noticed his fellow pilots had a problem: they’d gotten so used to following the magenta flight path lines on their fancy new navigation screens that they were forgetting how to actually fly the damn plane. He called them “children of the magenta line.”

Fast forward to now, and I can’t shake the feeling we’re watching the same movie play out in tech; except, the stakes are higher and no regulatory body forcing us to maintain our skills.


Look, I’m not here to tell you AI is bad. I use these tools daily. They’re genuinely useful in limited contexts. But when Dario Amodei (the dude running Anthropic, the company building Claude) goes on record saying AI could wipe out half of all entry-level white-collar jobs in the next few years and push unemployment to 10-20%, maybe we should pay attention.

“We, as the producers of this technology, have a duty and an obligation to be honest about what is coming,” he told Axios. “I don’t think this is on people’s radar.”

He’s not wrong.

The Data’s Already Ugly

Here’s what caught my attention while pulling this together:

Software developer employment for the 22-25 age bracket? Down almost 20% since ChatGPT dropped. Meanwhile, developers over 30 are doing fine. We’re not replacing jobs—we’re eliminating the ladder people used to climb into them.

More than half of engineering leaders are planning to hire fewer juniors because AI lets their senior folks handle the load. AWS’s CEO called this “one of the dumbest things I’ve ever heard” and asked the obvious question: who exactly is going to know anything in ten years?

And my personal favorite: a controlled study found developers using AI tools took 19% longer to complete tasks—while genuinely believing they were 20% faster. That’s a 39-point gap between vibes and reality.

Oh, and a Replit AI agent deleted someone’s entire production database during an explicit code freeze, then tried to cover its tracks by fabricating thousands of fake records. Cool cool cool.

What I Actually Wrote

The full paper traces this from that 1997 pilot observation through Dan Geer’s 2015 warnings (the man saw this coming a decade early) to the current mess. I dug into:

  • What the research actually shows vs. what the hype claims
  • Where aviation’s lessons translate and where we’re in uncharted territory
  • The security implications of AI-generated code (spoiler: not great)
  • What orgs, industries, and policymakers can actually do about it

This isn’t a “burn it all down” screed. It’s an attempt to think clearly about a transition that’s moving faster than our institutions can adapt.

The window to shape how this goes is still open. Probably not for long.


Grab the full PDF. Read it, argue with it, tell me where I’m wrong and what I missed in the comments.

ENISA published docs for their European Vulnerability Database (EUVD) — https://euvd.enisa.europa.eu/apidoc.

I’ve got an easier-on-the-eyes version that supports light/dark mode and includes sample API JSON results at https://rud.is/euvd-api/. The Quarto markdown source for it can be found at https://rud.is/euvd-api/euvd-api.qmd.

I need to make an MCP (Model Context Protocol) server for the API, but not everyone wants an MCP server, so there’s a TypeScript NPM package for it — https://www.npmjs.com/package/@hrbrmstr/euvd (source: https://codeberg.org/hrbrmstr/euvd-ts). This comes with the added benefit of making it easier/cleaner to build an MCP server. Friends don’t let friends make icky Python-based MCP servers.

I also need to integrate it into pipeline stuff at $WORK, so there’s also a Golang API wrapper & CLI @ https://codeberg.org/hrbrmstr/euvd.

READMEs in both repos have all the details.

Meet Suriest — a new REST API service for validating Suricata rules, designed to be run by organizations to streamline rule validation workflows. Suriest supports Suricata 6.0 and later and offers features like secure configuration, S3-compatible storage for logging validation attempts, and a simple HTTP API to validate rules programmatically. While the project is intended for deployment within your own environment, there’s a live instance already available for immediate use at https://sigchk.hrbrmstr.app/validate-rule. You can test it easily with a curl command like:

curl --silent --request POST --url https://sigchk.hrbrmstr.app/validate-rule \
  --header "Content-Type: application/json" \
  --data '{"rule": "alert http any any -> any any (msg:\"Test Rule\"; content:\"test\"; sid:1000001; rev:1;)"}'

This live service currently runs Suricata 7, since Suricata 8 is still in beta. For full details on setup, configuration options (including S3 logging), and API usage, check out the README in the repository at https://codeberg.org/hrbrmstr/suriest. Suriest offers a practical, scalable solution for Suricata rule validation that integrates well into security operations and development pipelines.