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?

I'm on the security team at a mid-size company. Fairly small security team, mostly AWS, and we're using Wiz for cloud security/CDR alongside our SIEM and EDR.

Curious how others are handling this because I'm not sure I'm getting the value I expected from CDR. We get the detections and cloud context, but at the end of the day someone still needs to investigate, jump between tools, figure out what actually happened and decide what to do.

We recently got pitched Sweet Security, which supposedly does a lot of that investigation automatically with their agentic investigator. Has anyone here actually deployed it in production?

And maybe a dumb question, but if Sweet is already doing the detection, investigating it and potentially taking response actions, how much of the Wiz CDR use case is it basically covering?
Are people running both? Or does one eventually make part of the other redundant?
Would love to hear from people actually using this :)

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.

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.

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?

[ 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?

Our team is evaluating data security posture management platforms, but our strict zero-egress requirement is making it tough. A lot of modern tools seem to force you into multi-tenant SaaS.

We have sensitive internal files and database records that cannot leave our network perimeter under any circumstances. Sending raw payload data out to a vendor cloud is an absolute non-starter for our security architecture board.

For anyone managing strict data residency requirements, what are the best Varonis alternatives you have actually deployed on-prem or in self-hosted environments?

Been asked to lead attack surface discovery and the first thing I see is CMDB, cloud inventory, DNS all disagree on what we own externally. we are lining up a poc for a few ASM tools, same limited seed list for each, and we keep quiet about some subsidiaries and vendor managed infra we already know about. Idea is to see who can link unknown asset discovery back to us with decent attribution instead of just scanning whatever we fed them

If you have done this kind of internet facing assets poc, how did you test that the surprise assets really belongs to you and not random noise?

Im just curious to get people's thoughts. In regulated environments... why do people think so highly of pentests and so little of compliance assessments, when they both do the same thing: determine if an organization is meeting its controls?

In all of the pentest engagements I have sat in, most of the findings are the same (unpatched server, app not upgraded, admin account creds cached on a box, legacy protocol not disabled). If orgs are not patching servers (or maybe they have a legacy box not being patched)... that would normally end up being caught on both engagements.

Both pentests and assessments are point in time. Pentests for my environment have typically been higher cost and only cover the technical side. So why do we look more favorably at one versus the other?

Weve had a hardening project open for three quarters now. The scanning half was easy and the cutting half keeps getting handed back, because no service owner wants to sign off that removing a package is safe. Im looking at tooling that produces runtime evidence for what an image touches so the decision stops being a judgement call. Six teams and no central platform group makes this political more than technical. How did you get service owners to accept a slimmer image?

For agents that can call tools, access internal data, send messages or change things in other systems, how are you putting guardrails around them?

I’m trying to understand where policy gets enforced, how teams handle prompt injection and sensitive data and where all of that shows up for security afterward. Are you handling this inside each app, through the existing security stack or with a separate layer for AI traffic?