Their main worry is agents being talked into "authorized-looking" actions. The framing that stood out: the main risk isn't the model making a mistake; it's an attacker instructing the agent to do something that looks authorized. Their approach is defense in depth: monitoring, auditing, guardrails, and a kill switch to shut down a misbehaving model.

Also in the piece: TII's CEO says an agent you can't monitor in real time "is malware that got access to your machine," and that non-human agents need clear identities. Kaspersky describes "living off local agents," where attackers use an agent already on the machine to find valuable data.

Source: www.khaleejtimes.com/business/tech/ai-agents-tricked-accessing-sensitive-government-data-cybersecurity

For those running agents in production: do your agents have their own identities and least-privilege scopes, or do they inherit a user's or service account's permissions? And who owns the kill switch?

I mean I understand that they were outdated and that someone figured out a way to exploit them, but shouldn't they be exposed on a local network. I guess that the attacker was suitably positioned.

Does any one has more info about this story ?

Hello everyone,

I am a researcher looking for an arXiv endorsement in the Cryptography and Security (cs.CR) category to submit my latest paper.

• Paper Title: A Comparative Evaluation of LLM-Based Cybersecurity Analysis:

Mistral-7B, Llama3.2, andLily-Cybersecurity on Live IDS Data

and Benchmark Datasets

• Field: Cybersecurity / cs.CR

To protect the confidentiality and intellectual property of the research, the abstract, GitHub link or the full draft are available.

If you are an eligible endorser in the cs.CR category and willing to help, please comment below or send me a DM. I would be glad to share the abstract and the full paper draft with you privately for review.

Thank you so much for your time and support!

I'm looking into vendors for a third-party security assessment and Sygnia and Kroll are two options I'm considering. Does anyone have any experience working with either of them? How was the engagement and did they meet your expectations

We've been evaluating Nucleus as a normalization and prioritization layer on top of our existing scanning stack, not a replacement for it, the scanners stay, we just wanted one place that actually makes sense of what they're all flagging. So far it's pulled in data from most of our stack (SAST, DAST, SCA, container scanning, CSPM, ASM, CMDB, ITSM) without us needing to build a custom integration for each source, which was more of a lift than expected going into this. The part we're still digging into is how the risk scoring adapts once you're past the initial setup. It's supposed to weight asset context and business impact together with exploit and malware evidence rather than just leaning on CVSS, which lines up with how we actually want to prioritize, but we want to see it hold up across a few more use cases before we call it settled. For anyone running Nucleus in a similar multi scanner setup, what did you end up tuning in the scoring model once you got past the default config?

We are using our IR playbooks and shadowing to bring new responders up to speed. I'm looking for something that gives them practice making calls before they join the on call rota. Reading the escalation path is easy. Knowing when to use it with half the facts is harder.

The main focus is deciding when to escalate and giving the incident lead a useful update. Reflex Security and Immersive Labs are on the list for their crisis simulations. Reflex scenarios adapt to team decisions which is the part Id want to test.

Senior responders would still review the reasoning afterward. The catch is prep time. If every short session needs someone to write a new scenario and spend an afternoon taking notes it wont happen often.

Given the case a support agent reads a ticket with instructions buried in it then tries to send a customer record to the wrong destination, Its final reply can look completely harmless. The tool call is the bit that matters.

Alice positions wonderfence as runtime guardrails.For an agent like this the first thing I'd want to pin down is where the check happens, a blocked response wouldn't undo a record already sent.

The tool itself would still need to check whether that user is allowed to access the record and send it to that destination. i'd want a test with a dodgy ticket (a normal ticket asking for the same permitted action) and a trace showing whether either tool actually ran.

For people adding guardrails around tool-using agents what evidence do you ask for to check that the risky action gets stopped without breaking ordinary requests?

Hi, we ran a tabletop after last year's vendor breach and my team froze because the plan assumed the vendor would answer in minutes, not 14 hours. I feel sick about it, because the whole exercise turned into us staring at the phone and asking each other what the actual next step was (pls tell me someone else has done this).

Hi mates! I’ve set up a homelab (enterprise network simulation) on my laptop (an OMEN 16, i7-14650HX, 32 GB RAM, RTX 4060 8 GB) for long-term IT practice and testing, focusing on networking, sysadmin, and pentesting. More details can be found in this GitHub repo: https://github.com/Ko0ler/HomeLab

I'd greatly appreciate your feedback!

Autonomous AI agents running intrusions from recon to root. The operator wrote a goal and read the results. This blog covers real AI attacks happening in the wild.

- 75 transcripts off attacker servers
- Claude Code on DeepSeek, pi, Codex
- command execution on 23 hosts
- SSRF to a root cloud box in 9h 44m
- unauth SQLi to OS commands
- Open WebUI rooted, CVE-2026-0766
- one target held 5 days, 304 steps
- card and wallet data stolen, wiped
- 150,574 AI gateways swept for keys
- in house 0day dev on gateway source

Reposting for visibility. My name is Stephanie Hull, and I am a doctoral student at National University. I am conducting an online survey to examine how organizational readiness influences the effectiveness of AI-driven cybersecurity in U.S. healthcare systems. In order to participate, you must be at least 18 years of age, currently employed in a cybersecurity, information technology, digital security analyst, or other related role, and working within a U.S. healthcare system.

The survey is anonymous and has approximately 45 questions and will take about 10–15 minutes to complete and will ask questions about technical infrastructure, workforce competence, financial resource allocation, AI cybersecurity implementation, and cybersecurity performance outcomes such as threat detection, response time, and system resilience.

Follow this link if you wish to participate in this voluntary research: https://ncu.co1.qualtrics.com/jfe/form/SV_1IatwkB184OOyHA. Please share this link with others!

Thank you for your time,

Stephanie Hull, Doctoral Student, National University

Email: [s.hull7960@student.nu.edu](mailto:s.hull7960@student.nu.edu)

Thoughts on MITRE ATTACK human interaction techniques mapped into a gamified way to train banking/telecom sector employees.

Did this project is worth it as an FYP?

if yes should we focus on training technical staff or non technical staff because platforms like immersive labs focus on IT technical employees.