If so, was the panic really necessary?

I looked at a bunch of PoCs, and it seems you need to do multiple consecutive requests to the same php worker process. In production systems that get a reasonable amount of traffic, you have multiple VMs/containers running multiple php processes behind a loadbalancer, and there will be constant background noise from legitimate traffic as well as scrapers and scanners.

The PoCs get around this because they experience zero traffic, know the exact number of processes, tie up all but one process with open connections and execute the attack against the remaining process.

On top of that, if you don't know the exact build, you need to try (assuming you have prepared exploits for every common build) exploits for the most popular x number of builds. So basically you would be DoS'ing a site for days or weeks, which would get noticed, and then you'll likely get WAF'd.

After spending crazy amount of time on remediation and then finding that the vuln is afaik significantly less scary than heartbleed/shellshock/log4shell, I'm confused. I did a little writeup explaining the above with more details (https://blog.luitjes.it/posts/i-want-my-wednesday-back/) but I keep wondering if I'm wrong (I do some infosec work occasionally but it's not like I'm a fulltime exploit dev).

I've seen a few posts saying this is hard to exploit because it needs to be customized per build, but nothing about having to DoS anything but the most low-traffic sites for days or weeks to get lucky enough to do two consecutive requests to the same php process.

Before my company allowed enterprise use of AI, I knew where my company's sensitive data lived, but now every department has adopted a different AI tool and no information is centralized any more. Leadership keeps asking whether we're protected, and I don't know if we even have complete visibility at this point. Is everyone else dealing with this, or have you found a process that's actually working?

So: what controls do you use to make sure a decommissioned agent's access is really gone, and how do you prove it after the fact?

Hello everyone

I've submitted a few vulnerability reports to VulnCheck recently. They're all different issues that I've tested and reproduced in my local lab, and I got a Submission ID for each one

I'm new to their disclosure process and was wondering if anyone here has dealt with them before

Do they usually send updates while reviewing reports, or do you only hear back once they've made a decision? And if a report gets rejected, marked as a duplicate, or doesn't qualify for a CVE, do they still let you know? Or do they only contact you when a CVE gets assigned

Also, is there any way to check the status of a submission using its ID? And roughly how long did it take you guys to hear back

Thanks

I'm looking at options for Cloud Attack Path Analysis solutions and am wondering what solutions are decent vs. total snake oil. Orca keeps coming up but I'd really wanna hear from people actually running it than from a sales deck. I'm going to speak to several but would like to know if anyone out there has had particularly good or bad experiences with any of them. Feel free to DM if you don't want to speak openly in public; I understand 😁

Just wanted to know if anyone has bought into this voluntary program from the DoD Cyber Crime Center that provides an extra layer of security for DoD suppliers network. Any reviews from implementation and its effectiveness?

An exposed C2 server tied to INC and The Gentlemen ransomware affiliates leaked 133 AI session logs (8,257 messages). A few things stood out:

→ The models were standard commercial LLMs accessed through OpenRouter. “WormGPT” was only a persona prompt.
→ When a model refused, the operators didn’t switch tools. They reworded the request as an “Authorized Red Team Assessment Request.”
→ The AI didn’t run on its own. Each campaign started with a victim-specific prompt telling it to “coordinate every action with me,” and the operator approved every step.
→ The tooling included 32 MCP servers covering everything from recon to ransom pricing.

The access methods weren’t new: FortiClient EMS, SimpleHelp, cPanel/WHM, and a lot of password reuse. 62 named organizations appear in the case files.

So the AI added speed and scale, not new techniques. That raises a fair question: is refusal-based safety meaningful when “it’s for a red team engagement” gets the job done?

Without a real help desk, MFA resets on small teams mostly come down to an admin hopping on a call and deciding the person seems legit.

The part that bugs me is where the request comes from. If you’re locked out of MFA you’re probably locked out of email and Slack too, so the ask shows up from a personal phone or Gmail. Same place an attacker would send it from.

What’s the minimum you’d require before resetting? Callback to a number on file, manager sign-off, something else entirely?

Hi, I need honest opinions on any of these vendors from people who tried them (if you're from any of these companies, skip. I don't want marketing bs): Dropezone, Torq, mate, prophet security.

New funding agreement says we have to report a data incident to the funder within seventy two hours of becoming aware and for a small charity on Microsoft 365 with one file server and no SIEM, it's the becoming aware part that worries me. If something happened in our tenant or on the file server I think we'd find out when a user told us and even then I'd struggle to work out what happened inside three days. If you've had to meet a clause like this, what did you put in place to notice and reconstruct an incident that fast?

We're shortlisting exposure management tools and Wiz-io is the front runner mostly because of a couple of engineers close contacts.

Looking for something that covers the external attack surface too and adds threat intel context. Anything that remediates needs to be careful tho as we're payments so production downtime is not an option.

What else should be on the list? Seen Palo Alto, Check Point and CrowdStrike mentioned so far, no demos yet. Thanks

Mod Approved

Hi everyone,

I'm an MSc Cyber Security student at Islington College, London Metropolitan University. My dissertation investigates burnout, cognitive workload and alert fatigue among people working in Security Operations Centres (SOCs) and related cybersecurity operations roles.

I'm looking for participants who currently work, or have worked within the last 12 months, as a SOC analyst, incident responder, threat hunter, or in a closely related role involving security monitoring, alert triage, threat detection, or SIEM/MDR operations.

The survey explores workplace factors that may contribute to burnout and workload in security operations. It's anonymous: no names, email addresses, IP addresses or employer details are collected, and results will be reported only in aggregate. Participation is voluntary, and you can stop at any time before submitting your responses.

Ethics approval: Islington College (London Metropolitan University)
Estimated time: 15–20 minutes
Survey link: SOC Burnout & Alert-Fatigue Risk Assessment Survey – Fill out form

If you meet the eligibility criteria, I'd really appreciate your participation. If you know someone who may be eligible, please feel free to share this post with them.

I'm happy to answer questions in the comments.

Thank you for your time and support!

Hey folks!
I’m looking into Cymulate, Picus, and Pentera for security validation and BAS, and wanted to see if anyone here has hands-on experience with these tools. How do they perform in real-world scenarios? Are they easy to set up, and are the results actually useful? How do they stack up against each other in terms of features, coverage, and overall value? Would love to hear your honest opinions, pros and cons, or anything worth knowing before making a decision. Thanks.

Howdy folks,
Considering implementing something risky. If I implement the below, is there still risk involved to make me second guess allowing this on a public facing website B2C.

planning enquiry attachments using Cloudflare Workers, accepting only JPEG/PNG images under 10 MB.
Originals go into private R2 quarantine as immutable uploads, are scanned by ClamAV in a separate container, then decoded and re-encoded into fresh JPEGs using Cloudflare Images, stripping metadata.
Only processed copies reach our staff admin viewer or email. Originals are deleted after successful processing; failed uploads remain inaccessible and expire.
Does this architecture make sense, and is ClamAV worthwhile alongside mandatory image re-encoding?

Enquiry form → Email code verification → Upload validation
→ Private R2 quarantine (immutable original + hash)
→ AV scan → Decode and re-encode as a new JPEG
→ Validate output → Save approved copy + enquiry link
→ Delete original

Approved copy → Staff admin viewer / optional email attachment

Failed checks → Block access → Retry or reject → Delete original

I build systems that receive payment confirmations from Safaricom’s M-PESA Daraja API (STK Push and C2B callbacks). As far as I can tell, Daraja doesn’t send an HMAC signature or similar with its callbacks, unlike Stripe or GitHub webhooks, so the receiving endpoint can’t cryptographically verify that a callback really came from Safaricom.

Current mitigations I’m aware of or using:

• Allowlisting Safaricom’s published callback IP ranges
• A long random secret token in the callback URL path
• Treating the callback only as a hint, then confirming the payment with an outbound Transaction Status query before marking anything paid
• Idempotency on the transaction ID so replayed callbacks can’t double-credit

My specific question: is the outbound “confirm before trust” query enough on its own to treat inbound callbacks as untrusted, or are there attack paths I’m missing (e.g. race conditions between callback and confirmation, or manipulation of the confirmation response)? How do others handle unsigned webhooks from payment providers in production?

Hi all, I'm trying to learn how infrastructure and network audits work in practice and would love some input from people doing this daily.
What are the control gaps you find most often?
Where do auditees tend to be weakest (patching, access reviews, firewall rules, logging)?
Any audit checklists you'd recommend

Last year we brought on a tool to get runtime context on our workloads and help prioritize vulns, mostly to cut noise based on what’s actually loaded in memory. At the time it seemed like a no brainer, since direct exploitation or even chained attacks mostly can’t work against components just sitting on the filesystem.

We ran a threat modeling exercise the other day on some new systems running AI agents with tool access, and in some cases a shell. It got me thinking: can’t the agent load anything it needs at any point, unlike older deterministic code? And if so, doesn’t runtime context based prioritization fall apart completely?

Can anyone with practical experience in this area chime in? I’m at my wits end brainstorming hypotheticals with GPT.

We're talking to vendors who keep pitching both, but I'm not sure if my company needs that because we're still a pretty small org. Can someone explain to me why we would budget for both, or if one would be preferable to the other?

Hi everyone,

Our application security workflow is increasingly AI-assisted from threat modeling and SAST/SCA to code reviews and vulnerability analysis. We use Claude, OpenAI, and Copilot to perform SAST with custom prompts, and source-code context along with internal tools[traditonal sast, jars moderation]. Burp Suite for manual validation.

But here's the challenge: when AI helps developers write code and AI helps security teams review it, how do we reliably catch vulnerabilities that escape both?

I'm looking for ways to improve coverage, independently validate findings.

For those working in AppSec, DevSecOps or AI-assisted security:

• How do you identify vulnerabilities that AI-driven reviews miss? If so, have you?
• What independent validation or testing mechanisms do you use?
• How do you automate repetitive security testing while keeping human expertise focused on high-risk issues?
• Have you built effective security agents or workflows that genuinely reduce manual effort?

I'd love to hear about your real-world approaches, automation strategies and lessons learned not just tool recommendations.

[ Removed by Reddit on account of violating the content policy. ]

My team stopped trusting the scanners and last month we hand-triaged every open finding on our three main services. I need to know if these numbers are normal.

That's like roughly 5% real overall with two engineers spending most of the month on this, we can’t keep doing it manually.

What filters out the noise and ranks what’s left before a human ever looks at it?

Every Monday i build the fix-these-first list and the two scanners barely agree on what belongs at the top of it, which for a long time i put down to one of them being bad.

Then i took a single host with a single CVE and diffed the two findings field by field, one said 9.8 and the other said 5.3, and the gap turned out to be three separate things stacked on top of each other.

The scanner calling it critical has no credentials on that subnet so its fingerprinting a version and cant see whether the patch landed, and the other one can, which explains a chunk of it on its own.

On top of that the word critical doesnt mean the same thing in both consoles, one thresholds at 7.0 and the other at 9.0, and one weights EPSS and known-exploited data while the other is pure CVSS.

Both vendors closed my tickets as working intended and both were right, which is the least useful outcome available.

We wrote a reconciliation rule, it runs in axonius where both scanner feeds land, and now we report a number that matches neither console.

How do others handle this, and does anyone have a method that survives scrutiny.

Hey guys, I’ve recently been reading papers that use LLMs to understand code logic and infer security-related semantics from source code and comments.

I’m curious how people with hands-on experience in static analysis or white-box security testing view these approaches.

1. Can LLMs actually help overcome limitations of traditional static analysis, such as complex inter-procedural data flows?

2. How reliable is LLM-based code understanding? Could generating multiple possible interpretations and verifying them work? If so, how would you validate them?

3. For agent-driven static analysis, how do you ensure reasonably consistent results across multiple runs on the same codebase?

For those who have experimented with LLM-assisted code analysis, what have you found genuinely useful, and where do these approaches fall short?

For teams managing PKI across a mix of on-premises servers, cloud workloads, and internal applications, how do you identify certificates that aren't already tracked by your central certificate inventory?

I'm particularly interested in certificates issued by internal CAs, self-signed certificates, and certificates deployed manually by individual teams. These can be easy to miss if discovery relies only on the systems connected to your certificate management platform.

I've been looking into approaches to certificate lifecycle management and cryptographic discovery, including work in this area by companies such as Encryption Consulting. I'm trying to understand how these approaches translate into day-to-day security operations rather than just what the tools promise.

Do you get reliable coverage through authenticated asset discovery, network scanning, CA inventory audits, or a combination of these methods?

Also, how do you distinguish between a certificate that's simply missing from the inventory and one that represents an actual security risk, such as an unexpected certificate, weak configuration, or an untracked private key?

I'd appreciate specific technical approaches that have worked well in larger environments, including any limitations you've encountered.

Everyone worries about people pasting into chatgpt. For us the problem was access. Someone wired an agent into the drive to save time and it could read every client's folder, the one it needed and all the others. Blocking the consumer tools did nothing there as the agent was approved.

We scoped what each AI thing can reach and a co worker only sees the records for the job its doing and the rest stays invisible to it. How are people governing their AI tools and agents can touch, short of locking it all down?