Standard advice for refresh tokens: rotate on every use, store hashed, set a short expiry. Done, right?
Not quite.
Rotation alone does nothing against token theft. If malware or XSS lifts a refresh token from a legit client, the attacker and the client race to rotate it next. Whoever loses the race gets a "token revoked" error — and the winner keeps the session.
From the server’s point of view, it just sees two valid requests seconds apart. No alarm, no signal, nothing.
The missing piece is what OAuth 2.0 Security BCP §4.14 calls refresh token reuse detection: if a token that was already rotated is presented again, treat it as evidence of compromise and invalidate the entire session.
The core idea
Every token belongs to a family (FamilyId), shared across all rotations of a single login.
If a rotated token shows up again (outside a small grace window), you revoke the entire family:
the attacker is locked out
the legit user is forced to re-authenticate
the session is no longer silently compromised
if (stored.ReplacedByTokenHash is not null && stored.RevokedAtUtc.HasValue) { var withinGrace = stored.RevokedAtUtc.Value.AddSeconds(graceSeconds) > DateTime.UtcNow; if (withinGrace) return Fail("token_recently_rotated"); // benign race (SPA tabs, retries) await RevokeFamilyAsync(stored.FamilyId, ip, reason: "reuse_detected"); return Fail("token_reuse_detected"); }
Client-side it’s just one extra branch:
if (error.code === "token_reuse_detected") { // "You've been signed out for security reasons. Please log in again." router.push("/login?reason=compromised"); }
You can also hook into it for observability (alerts, SIEM, etc.):
Race vs theft look identical. Two requests with the same token arrive. One is legit, one might not be. Only timing differs. Grace window too small → false positives on flaky networks. Too large → real attack window. ~30 seconds worked well in practice.
Revoking the whole chain. On reuse you must invalidate all still-active tokens from that session. A simple FamilyId + index makes this a single bulk update.
Concurrency is common. Multi-tab SPAs, retries, mobile reconnects — without a grace window, I was logging myself out constantly during tests.
Looking back at this milestone video, it's the audience question towards the end I liked most: "are you happy"? Charlotte and I have chosen a path that's non-traditional, intense and at times, pretty stressful. There's no clear delineation of when work starts and ends, no holidays where we don't work, nor weekends, birthdays or Christmases. But we do so on our terms. It gives us a life of means and choices, one with excitement and adventure, and, above all, one with purpose, where we feel like we're doing something that makes a meaningful difference. I hope you enjoy this week's video, it's more personal than usual, but yeah, that's kinda what you do at milestones 😊
Perforce is source control software used in games, entertainment, and a few engineering sectors. It's particularly useful when large binary assets need to be stored alongside source code. It handles binary assets much better than Git, IMO. However, its one weakness is its terrible security defaults. You will die a bit inside when you see the out-of-the-box behaviour: "Don't have an account? Let me make one for you!" and "Oh, you didn't know by default there is a hidden, read-only 'remote' user that allows read access to everything? Oops!"
I scanned 6,122 public Perforce servers last year. 72% were exposing source code, 21% had passwordless accounts, and 4% had unprotected superusers (which allow RCE). The vendor patched the largest issue, but a significant portion are still vulnerable.
Hardening is a pain, but here it is summed up: p4 configure set security=4 # disables the built-in 'remote' user + strong auth p4 configure set dm.user.noautocreate=2 # kills auto-signup p4 configure set dm.user.setinitialpasswd=0 # users cannot self-set first password p4 configure set dm.user.resetpassword=1 # force password reset flow p4 configure set dm.info.hide=1 # hide server license, internal IP, root path p4 configure set run.users.authorize=1 # user listing requires auth p4 configure set dm.user.hideinvalid=1 # no hints on bad login p4 configure set dm.keys.hide=2 # hide stored key/value pairs from non-admins p4 configure set server.rolechecks=1 # prevent P4AUTH misuse
Years before the figure skater became an Olympic superstar, a Chinese operative tried to stalk her father and monitored other US residents deemed dissidents against China. And that’s just the beginning.
Most writeups of BlueHammer describe what it does. I read the actual PoC (FunnyApp.cpp, ~100KB of C++) and the most important line isn't in the oplock setup, the NT object namespace redirect, or the Cloud Files freeze. It's a comment.
The filestoleak array ships with one target active and two commented out:
SAM alone is a partial dump. The hashes are encrypted with the boot key — which lives in SYSTEM. Without SYSTEM you have ciphertext. With SAM + SYSTEM you have NTLM hashes you can pass-the-hash or crack offline. SECURITY adds LSA secrets: service account credentials, cached domain logon hashes, DPAPI master keys.
The complete credential package is two uncommented lines away from the published PoC. The author wrote both lines and chose what to ship.
Full analysis walks the actual code: the batch oplock on RstrtMgr.dll (not the EICAR file — that's what most writeups get wrong), the NtCreateSymbolicLinkObject swap in the session object namespace (not NTFS symlinks — a different layer entirely), the Cloud Files freeze via a fake OneDrive sync provider named IHATEMICROSOFT, and the undocumented IMpService RPC endpoint that triggers the chain with no elevated privilege required.
I love cutting-edge tech, but I hate hyperbole, so I find AI to be a real paradox. Somewhere in that whole mess of overnight influencers, disinformation and ludicrous claims is some real "gold" - AI stuff that's genuinely useful and makes a meaningful difference. This blog post cuts straight to the good stuff, specifically how you can use AI with Have I Been Pwned to do some pretty cool things. I'll be showing examples based on OpenClaw running on the Mac Mini in the hero shot, but they're applicable to other agents that turn HIBP's data into more insightful analysis.
So, let me talk about what you can do right now, what we're working on and what you'll be able to do in the future.
Model Context Protocol (MCP)
A quick MCP primer first: Anthropic came up with the idea of building a protocol that could connect systems to AI apps, and thus the Model Context Protocol was born:
Using MCP, AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts)—enabling them to access key information and perform tasks.
If I'm honest, I'm a bit on the fence as to how useful this really is (and I'm not alone), but creating it was a no-brainer, so we now have an MCP server for HIBP:
https://haveibeenpwned.com/mcp
You can't just make an HTTP GET to the endpoint, but you can ask your favourite AI tool to explain what it does:
In other words, all the stuff we describe in the API docs 🙂 That's an overly simplistic statement, and there are many nuances MCP introduces beyond a computer reading docs intended for humans, but the point is that we've implemented MCP and it's there if you want it. Which means you can easily use the JSON below to, for example, extend GitHub Copilot:
This is really the point of the whole thing - how can humans use it to do genuinely useful stuff? In particular, how can they use it to do stuff that was hard to do before, and how can "normies" (non-technical folks) use it to do stuff they previously needed developers for? I've been toying with these questions for a while now. Here's what I've come up with:
Firstly, I'm going to do all these demos on OpenClaw. I've been talking a lot about that on my weekly live streams over the past month, and the "agentic" nature of it (being able to act as an independent agent tying together multiple otherwise independent acts) is enormously powerful. Every company worth its AI salt is now focusing on building out agentic AI so whilst I'm using OpenClaw for these demos, you'll be able to do exactly the same thing in your platform of choice either now or in the very near future.
I'm using a Telegram bot as my interface into OpenClaw, let's kick it off:
Easy, right? 🙂 There's a different discussion around how secrets are stored and protected, but that's a story for another time (and is also obviously dependent on your agent). But the key is easily rotated on the HIBP dashboard anyway. If you don't have a key already, go and take out a subscription (they start at a few bucks a month), and you'll be up and running in no time.
Now that I know I'm connected, let's learn about how I'm presently using the service:
Most of these are pretty obvious, but I've also included another here that I use to monitor how the service is behaving with a large organisation. It's a real domain with real data, so I'm going to obfuscate it to preserve privacy, but it's a great demonstration of how useful AI is. In fact, the inspiration of this blog post was when I received this notification last week:
One of the most asked questions after someone in a large org receives an email like this is "who are those 16 people in the breach"? Because we can't reliably filter large domains in the UI, I'd normally suggest they either download the CSV or JSON format in the dashboard, then search for "Hallmark" in there or use the API and write some code. But now, there's a much easier way:
Well that was easy 😎 I like the additional context too, and now it has me curious: what have these people been up to?
Because I'm on a Pro plan (or if you're still on the old Pwned 5 plan), I've also got access to stealer logs. Let's see what's going on there:
If you were running an online service, that first number would indicate compromised customers. But as OpenClaw has suggested here, the second number is the one that's interesting in terms of employees entering their data into other websites using the corporate email address. But they'd never reuse the same password as the work one, right? 🤔 Best check which services they're entering organisational assets into:
The first one makes sense and is extra worrying when you consider these are people infected with infostealers. That's not necessarily malware on a corporate asset; they could always be using an infected personal device to sign into a corporate asset... ok, that's also pretty bad! I was a bit surprised to see Steam in there TBH - who's using their corporate email address to sign into a gaming platform?! A quiet chat with them might be in order. And the bamboozled.net stuff is weird, I want to understand a bit more about that:
Now I'm losing interest in this blog post and am really curious as to what's actually in the data!
Ok, so there's an entire rabbit hole over there! Let's park that, but think about how useful information like this is to infosec teams when you can pull it so easily. Or how useful info like this is to HR teams 😬
Keep in mind, these are corporate addresses tied to the company and are the company's property, so, yeah...
But remember the agentic nature of OpenClaw means we can ask it to go off and run tasks in the background, tasks like this:
This was just a little thought experiment I set up a few days ago and forgot about until yesterday, when I loaded a new breach:
I never asked it to look for "functional/system accounts"; it just decided that was relevant. And it is - this breach clearly had a lot of data in it related to purchases of services, which is an interesting aspect.
The idea of running stuff on a schedule opens up a whole raft of new opportunities. For example, monitoring your family's email addresses: "let me know when mum@example.com appears in a new breach". From here, your creativity is the only limit (and even that statement is debatable, given how much stuff AI agents come up with on their own). For example, creating visualisations of the data:
I could go on and on (I started going down another rabbit hole of having it generate executive-level reports with all the data), but you get the idea.
The AI Pipeline
This is about what's in our pipeline, and the primary theme is putting tooling where it's more easily accessible to the masses. Creating a connector in Claude, an app in ChatGPT, and similar plumbing in the other big players' AI tools is an obvious next step. This will likely involve adding an OAuth layer to HIBP, allowing end users to configure the respective tools to query those HIBP APIs under their identity and achieve the same results as above, but built into the "traditional" AI tooling in a way people are familiar with.
Future
A big part of this is about AI enabling more human conversations to achieve technical outcomes. I spotted this from Cloudflare just yesterday, and it's a perfect example of just this:
Cloudflare dashboard can now complete tasks for you.
- "Create a Worker and bind a new R2 bucket to it" - "Change my DNS records to 1.1.1.1" - "How many errors have happened this week"
Not only do we tell you, but we show you with generative UI.
I've been pretty blown away by both how easy this process has been and how much insight I've been able to draw from data I've been sitting on for ages. We'll be building out more tooling and easily reproducible demos in the future, and I'm sure a lot of that will do stuff we haven't even thought of yet. If you give this a go and find other awesome use cases, please leave a comment and tell me what you've done, especially if you've cut through the hyperbole and created some genuinely awesome stuff 😎
Two day intrusion. RDP brute force with a company specific wordlist, Cobalt Strike, and a custom Rust exfiltration platform (RustyRocket) that connected to over 6,900 unique Cloudflare IPs over 443 to pull data from every reachable host over SMB.
Recovered the operator README documenting three operating modes and a companion pivoting proxy for segmented networks.
Personalized extortion notes addressed by name to each employee with separate templates for leadership and staff.
Writeup includes screen recordings of the intrusion, full negotiation chat from their Tor portal, timeline, and IOCs.
u/albinowax ’s work on request smuggling has always inspired me. I’ve followed his research, watched his talks at DEFCON and BlackHat, and spent time experimenting with his labs and tooling.
Coming from a web security background, I’ve explored vulnerabilities both from a black-box and white-box perspective — understanding not just how to exploit them, but also the exact lines of code responsible for issues like SQLi, XSS, and broken access control.
Request smuggling, however, always felt different. It remained something I could detect and exploit… but never fully trace down to its root cause in real-world server implementations.
A few months ago, I decided to go deeper into networking and protocol internals, and now, months later, I can say that I “might” have figured out how the internet works😂 This research on HAProxy (HTTP/3, standalone mode) is the result of that journey — finally connecting the dots between protocol behavior and the actual code paths leading to the bug.
I submitted an earlier version of this dataset and was declined on the basis of missing methodology and unverifiable provenance. The feedback was fair. The documentation has since been rewritten to address it directly, and I would very much appreciate a second look.
What the dataset contains
101,032 samples in total, balanced 1:1 attack to benign.
Attack samples (50,516) across 27 categories sourced from over 55 published papers and disclosed vulnerabilities. Coverage spans:
Classical injection - direct override, indirect via documents, tool-call injection, system prompt extraction
Adversarial suffixes - GCG, AutoDAN, Beast
Cross-modal delivery - text with image, document, audio, and combined payloads across three and four modalities
Media-surface attacks - audio ASR divergence, chart and diagram injection, PDF active content, instruction-hierarchy spoofing
Benign samples (50,516) are drawn from Stanford Alpaca, WildChat, MS-COCO 2017, Wikipedia (English), and LibriSpeech. The benign set is matched to the surface characteristics of the attack set so that classifiers must learn genuine injection structure rather than stylistic artefacts.
Methodology
The previous README lacked this section entirely. The current version documents the following:
Scope definition. Prompt injection is defined per Greshake et al. and OWASP LLM01 as runtime text that overrides or redirects model behaviour. Pure harmful-content requests without override framing are explicitly excluded.
Four-layer construction. Hand-crafted seeds, PyRIT template expansion, cross-modal delivery matrix, and matched benign collection. Each layer documents the tool used, the paper referenced, and the design decision behind it.
Label assignment. Labels are assigned by construction at the category level rather than through per-sample human review. This is stated plainly rather than overclaimed.
Benign edge-case design. The ten vocabulary clusters used to reduce false positives on security-adjacent language are documented individually.
Quality control. Deduplication audit results are included: zero duplicate texts in the benign pool, zero benign texts appearing in attacks, one documented legacy duplicate cluster with cause noted.
Known limitations. Six limitations are stated explicitly: text-based multimodal representation, hand-crafted seed counts, English-skewed benign pool, no inter-rater reliability score, ASR figures sourced from original papers rather than re-measured, and small v4 seed counts for emerging categories.
Reproducibility
Generators are deterministic (random.seed(42)). Running them reproduces the published dataset exactly. Every sample carries attack_source and attack_reference fields with arXiv or CVE links. A reviewer can select any sample, follow the citation, and verify that the attack class is documented in the literature.
Comparison to existing datasets
The README includes a comparison table against deepset (500 samples), jackhhao (2,600), Tensor Trust (126k from an adversarial game), HackAPrompt (600k from competition data), and InjectAgent (1,054). The gap this dataset aims to fill is multimodal cross-delivery combinations and emerging agentic attack categories, neither of which exists at scale in current public datasets.
What this is not
To be direct: this is not a peer-reviewed paper. The README is documentation at the level expected of a serious open dataset submission - methodology, sourcing, limitations, and reproducibility - but it does not replace academic publication. If that bar is a requirement for r/netsec specifically, that is reasonable and I will accept the feedback.
I am happy to answer questions about any construction decision, provide verification scripts for specific categories, or discuss where the methodology falls short.
I'm starting to become pretty fond of Bruce. Actually, I've had a bit of an epiphany: an AI assistant like Bruce isn't just about auto-responding to tickets in an entirely autonomous manner; it's also pretty awesome at responding with just a little bit of human assistance. Charlotte and I both replied to some tickets today that were way too specific for Bruce to ever do on his own, but by feeding in just a little bit of additional info (such as the number of domains someone was presently monitoring), Bruce was able to construct a really good reply and "own" the ticket. So maybe that's the sweet spot: auto-reply to the really obvious stuff and then take just a little human input on everything else.
The current version of RAGFlow, a widely-deployed Retrieval Augmented Generation solution, contains a post-auth vulnerability that allows for arbitrary code execution.
This post includes a POC, walkthrough and patch.
The TL;DR is to make sure your RAGFlow instances aren't on the public internet, that you have the minimum number of necessary users, and that those user accounts are protected by complex passwords. (This is especially true if you're using Infinity for storage.)
Hi everyone, I’m a Cybersecurity student at HFU in Germany and recently submitted a vulnerability to the Google VRP regarding the Google Password Manager on Android (tested on Pixel 8, Android 16).
The Issue: When you view a cleartext password in the app and minimize it, the app fails to apply FLAG_SECURE or blur the background. When opening the "Recent Apps" (Task Switcher), the cleartext password is fully visible in the preview, even though the app actively overlays a "Enter your screen lock" biometric prompt in the foreground. It basically renders its own secondary biometric lock completely useless.
Google's Response: Google closed the report as Won't Fix (Intended Behavior). Their threat model assumes that if an attacker has physical access to an unlocked device, it's game over.
The BSI Discrepancy: What makes this interesting is that the German Federal Office for Information Security (BSI) recently published a study on Password Managers. In their Threat Model A02 ("Attacker has temporary access to the unlocked device"), they explicitly mandate that sensitive content MUST be protected from background snapshots/screenshots. So while Google says this is intended, national security guidelines classify this as a vulnerability. (For comparison: The iOS built-in password manager instantly blurs the screen when losing focus).
What are your thoughts on this? Should password managers protect against shoulder surfing via the Task Switcher, or is Google right to rely solely on the OS lockscreen?
AI coding tools are being shipped fast. In too many cases, basic security is not keeping up.
In our latest research, we found the same sandbox trust-boundary failure pattern across tools from Anthropic, Google, and OpenAI. Anthropic fixed and engaged quickly (CVE-2026-25725). Google did not ship a fix by disclosure. OpenAI closed the report as informational and did not address the core architectural issue.
That gap in response says a lot about vendor security posture.
As strikes continue on Iran’s nuclear facilities, the real danger isn’t the explosion, but what happens if critical safety systems fail—and how that risk could spread across the Gulf.
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