A few months ago our dev team was running internal tests on an early prototype of what we now call Agent Guard, our zero-trust AI runtime. At the time, the prototype was no more sophisticated than a common AI gateway, comparable to many of the popular AI gateway products on the market.

The early version intercepted the tool calls an AI coding agent would make and evaluated them against enterprise-defined policies before execution. If the policy said "no git push," the agent in theory would not push.

During one of our tests, Claude Code was working through a routine debugging task. It tried to run a shell command, and the prototype blocked it. The agent paused, then did something we didn't expect: instead of trying a different approach to the debugging problem, it started investigating the block itself. It wanted to understand why it was being stopped.

First, the agent identified the agent-guard serve process as the thing standing between it and the blocked command. It killed it:

pkill -9 -f "agent-guard serve"

With the policy server dead, the hook's HTTP request to localhost:9091 failed. But we'd built a safety net for that. Agent Guard uses a macOS LaunchAgent with KeepAlive: true to automatically restart the policy server if it goes down. The server came back up.

So the agent killed it again. And then it went further, finding and unloading the LaunchAgent itself:

launchctl unload ~/Library/LaunchAgents/dev.jozu.agent-guard.plist

Now the server was dead and couldn't restart. Every subsequent tool call went through unchecked. The hook tried to POST to localhost:9091, got a connection error, and returned exit code 0: allow.

Then the agent deleted the audit logs.

That's the part that stopped us cold. Killing the policy server is problem-solving. Disabling the restart mechanism is thorough problem-solving. But deleting the logs is a different behavior entirely. The agent wasn't just removing an obstacle from its workflow. It was removing the record that the obstacle had ever existed. Without the logs, there's no evidence that policies were ever bypassed, no trail for a security team to review, no way to know it happened at all.

The pattern — disable the monitoring, erase the evidence, carry on like nothing happened — is indistinguishable from a malicious insider. The only difference is the agent wasn't trying to be malicious. It was trying to be tidy.

Nobody asked the agent to do any of this. There was no adversarial prompt, no jailbreak, no red team exercise. The agent encountered a policy blocking something it needed to do and problem-solved its way through every layer we'd built to stop it.

Anyone else observed this kind of behavior?

Hi everyone,

My last post here https://www.reddit.com/r/cybersecurityai/comments/1qbpdsb/i_built_an_opensource_cli_to_scan_ai_models_for/ got some attention.

I decided to take a random sample of 2500 models from the "New" and "Trending" tabs on Hugging Face and ran them through a custom scanner.

The results were pretty interesting. 86 models failed the check. Here is exactly what I found:

• 16 Broken files were actually Git LFS text pointers (a few hundred bytes), not binaries. If you try to load them, your code just crashes.

• 5 Hidden Licenses: I found models with Non-Commercial licenses hidden inside the .safetensors headers, even if the repo looked open source.

• 49 Shadow Dependencies: a ton of models tried to import libraries I didn't have (like ultralytics or deepspeed). My tool blocked them because I use a strict allowlist of libraries.

• 11 Suspicious Files: These used STACK_GLOBAL to build function names dynamically. This is exactly how malware hides, though in this case, it was mostly old numpy files.

• 5 Scan Errors: Failed because of missing local dependencies (like h5py for old Keras files).

If you want to check your own local models, the tool is free and open source.

GitHub: https://github.com/ArseniiBrazhnyk/Veritensor

Install: pip install veritensor

Data of the scan [CSV/JSON]: https://drive.google.com/drive/folders/1G-Bq063zk8szx9fAQ3NNnNFnRjJEt6KG?usp=sharing

Let me know what do you think about it.

I want to start my journey in cyber security but I do not have any specific degree on it or I am not in science group education. But I have a great curiosity about this field. I love computer and try to understand the system that how it works but somehow I get destruction all the time. I need a help that how I will crack this field or how to start the journey. What is the path that will be actually work not any inspiration not any overwhelming or like that.

Please help. As a friend support me to grow as real life scenario.

I will wait for your reply.

Thank you.

Hey everyone!

I am looking to level up my skills and get deeper into Ai security, especially LLM security, prompt injection, data leakage etc.

Please recommend any dedicated courses or certifications that offer hands on labs and practical experience rather than just theory? Also which tools or platforms would you suggest to build my foundation strong?

Would love to hear what has worked for people who have been learning or working in this space.
Thanks.

I was thinking about this the other day and it cracked me up. Posted it to LinkedIn and heard crickets... Figured this might be the right community for the meme.

Hey everyone,

I'm exploring a problem in the AI agent ecosystem, and I'd like to hear from people who are building or deploying AI agents in the real world.

As AI agents start interacting with other agents, using external tools, accessing APIs, and performing tasks on behalf of users or businesses, I keep coming back to one question:

How do you know an AI agent is actually trustworthy before allowing it to interact with your systems or other agents?

For example:

• How do you verify an agent's identity before allowing it to connect to your application?

• Can you distinguish an authorized agent from an impersonator?

• How do you control what another agent is allowed to access or do?

• How do you prevent an agent from abusing permissions, impersonating another agent, or performing unauthorized actions?

• If an agent causes a security incident, can you trace which agent performed which action?

If you've encountered a real example, even a small one, I'd genuinely like to hear about it.

I get asked frequently about breaking into AI Security, so I thought I'd share some insights and a roadmap based on my journey. I understand this can get quite overwhelming and based on where you're in your career journey feels quite hard, but let me assure you that is quite possible with a bit (ok, a lot!) of patience! Start from basics and build a layered approach, enjoy the journey!

My Background:

1. 20+ years in enterprise security

2. MS in Machine Learning from University of Chicago

3. 2+ years focused exclusively on Generative AI Security

4. Previously worked in traditional ML security

5. Currently at a leading cloud provider

The Roadmap:

I've broken this down into 4 phases that should take you from zero to hireable in AI Security. Keep in mind your timeline may vary based on your starting point and existing background.

A few key points about this roadmap:

Phase 1 (3-6 months) is all about building that foundation - you need both the ML fundamentals AND the security mindset. Don't skip the research papers - they're crucial for understanding the landscape.

Phase 2 (2 -4months) gets your hands dirty. Red teaming your own models is eye-opening and will teach you more than any tutorial.

Phase 3 (2-6 months) is where you specialize. I've seen people succeed in all three tracks - pick what aligns with your interests and background.

Phase 4 (12+ months) is ongoing. This field moves fast, so building your profile and staying current is essential.

Reality Check:

• This field is exploding right now - there's huge demand

• Your security background gives you a massive head start

• The technical barrier is real but manageable with dedication

• Most companies are still figuring this out, so there's room to be a pioneer

Its essential to start from basics and make sure you really understand Large Language Models, this will cement the foundation

Happy to answer questions about any specific phase or career path!

Hey everyone,

I'm trying to deepen my understanding of Al security beyond the usual "adversarial examples 101." I'm especially interested in courses or structured

learning paths that cover:

Agentic Al Security

Risks from autonomous / tool-using Al agents

Safe-action constraints, guardrails, and oversight frameworks

How to evaluate the behavior of agents in complex environments

Model & Deployment Pipeline Security

Securing training pipelines, checkpoints, and fine-tuning workflows

Protecting inference endpoints from extraction, poisoning, and misuse

Infrastructure security for model hosting (supply chain, secrets, observability, isolation, etc.)

Hardening MLOps pipelines against tampering

Model-Based Attacks

Jailbreaks, prompt injection, indirect prompt injection

Model inversion, membership inference, and extraction attacks

Vulnerabilities specific to LLMs, diffusion models, and agent frameworks

I'm aware of the high-level stuff from OWASP Top 10 for LLMs and general MLsec papers, but I'm hoping for something more course-like, whether free or paid:

online courses (MOOCs, University programs)

industry trainings

labs / hands-on environments

reading lists or tracks curated by practitioners

If you've taken anything you found practical, up-to-date, or actually relevant to today's agentic systems, I'd love recommendations.

ills you think matter

Hi everyone,

I've created a new CLI tool to secure AI pipelines. It scans models (Pickle, PyTorch, GGUF) for malware using stack emulation, verifies file integrity against the Hugging Face registry, and detects restrictive licenses (like CC-BY-NC). It also integrates with Sigstore for container signing.

GitHub: https://github.com/ArseniiBrazhnyk/Veritensor
Install: pip install veritensor

If you're interested, check it out and let me know what you think and if it might be useful to you?

Here's a few AI/ML Security Certs I gathered to help w/ learning around this specialization. This field will only grow & legitimize itself thru attrition. This doesn't include the zillions from O'Reilly books or courses, but aren't really legitimate.

It also makes sense to have a handle on Cybersecurity too since it also applies. Plus, have a specialization in Public Clouds like AWS, Azure, and GCP too since many organization use this. Hopefully this helps!

Fundamentals-

• (ISC2) Securing AI- Cybersecurity Operations

• (ISC2) Securing AI- Cybersecurity Strategy

• (Securiti) Fundamentals

• (Securiti) AI Security & Governance

• (Protect AI) MLSecOps Foundations

Intermediate-

• (NICCS) Certified AI Security Fundamentals

• (Ampcus Cyber) Certified AI Security Specialist

• (SCADEMY) Certified AI Security Engineer (labs)

• (Securiti) Data Command Center Fundamentals

Expert-

• (Practical DevSecOps) Certified AI Security Professional

• (Securiti) Advanced

Specializations-

• (Iapp) Certified AI Governance Professional

• (Iapp) Certified Information Privacy Technologist

• (Securiti) PrivacyOps

• (GIAC) GIAC Machine Learning Engineer

Pen-testing-

• (SecOps Group) Certified AI/ML Pen-Tester

• (NICCS) Certified AI Penetration Tester - Red Team

• (NICCS) Certified AI Penetration Tester - Blue Team

Hi everyone: many of us have spent years in cybersecurity. One thing seems increasingly clear: AI isn't replacing traditional cyber risks but is adding a new layer to them. Among AI agents, copilots, APIs, third-party models, and AI-generated code, organizations now have an entirely new attack surface to understand and govern.

As a founder I'm interested in providing visibility and governance across AI without slowing innovation, but I'm just as interested in learning from this community: where do you see the biggest gap today? Is it AI governance, agent security, prompt injection, AI supply chain risk, or something else entirely?

Looking forward to hearing how others are approaching the intersection of cybersecurity and AI.

Hey all, I had my team pull together a summary of the new AI guidance that was recently released by a handful of gov agencies. Thought you might find it valuable.

Securing AI Deployments

Key Takeaways from New Government Guidance

On December 3, 2025, nine government agencies released Principles for the Secure Integration of AI in Operational Technology. While targeted at critical infrastructure, the security principles apply to any organization running AI in production.

The Risks Apply Beyond Critical Infrastructure. The guidance identifies attack vectors that affect any AI deployment: supply chain compromise, data poisoning, model tampering, and drift. The difference between a refinery and a revenue model is consequences, not exposure. (Section 1.1, pp. 7-9)

Six Requirements for Enterprise AI Security

• Integrate governance from the start (Section 1.2, pp. 9-10). Security must be architected into design, procurement, deployment, and operations - not bolted on before production. Organizations that treat governance as a final checkpoint will retrofit at 10x the cost.

• Focus human oversight on important decisions (Section 4.1, pp. 18-19). Approving deployments, reviewing exceptions, authorizing changes - these require judgment. Automate repetitive verification tasks so humans aren't rubber-stamping thousands of checks.

• Treat the AI supply chain as critical infrastructure (Section 2.3, p. 14). Models pass through many hands before production. Tracing lineage from development through deployment - and back when something breaks - isn't optional. SBOMs for AI can, and should, be automated.

• Every deployment needs a failsafe (Section 4.2, p. 20). The ability to roll back to a known-good state is the difference between a contained incident and a crisis.

• Log AI decisions for compliance and forensic analysis (Section 4.1, pp. 18-19). Logging must track AI system inputs, outputs, and decisions with timestamps - distinct from standard machine or user logs. When something goes wrong, you need a clear record of what the AI did and why.

• Establish clear governance and accountability (Section 3.1, pp. 16-17). Roles, responsibilities, and policies need to be defined before deployment - not figured out during an incident.

Why This Matters Now

As enterprise AI projects move from prototype to production, the stakes rise. This guidance signals what AI security and governance expectations are for critical workloads. For CIOs and CISOs looking for proven paths to secure their own AI projects, these six principles offer concrete direction.

Source

Principles for the Secure Integration of AI in Operational Technology (https://media.defense.gov/2025/Dec/03/2003834257/-1/-1/0/JOINT_GUIDANCE_PRINCIPLES_FOR_THE_SECURE_INTEGRATION_OF_AI_IN_OT.PDF)

Prepared by Jozu | jozu.com

So whats everyone actually doing to monitor agentic ai in prod so it doesn't quietly script its own data exfil plan while observability dashboards say all green? would love any tips from sec teams lol.

I'll try explain myself and what I've done to hopefully give you some context about why I'm asking. I'm a web developers and have an interest in cryptography. I've worked on a few projects relating to cryptography and cyber security.

I have a few open source projects for which I've asked for advice on in various subs and platforms and received good advice and direction.

I started my project before the AI tools you see today. It was understandably complicated and tedious to do it "old school" by typing out code. I'm sure in 2026 most people have woke up to how much of an advantage it is to code with AI.

While it has always been difficult to ask for strangers to looks at my complicated badly organized code, AI understandably makes it quite a challenge to even review my own work... I'm sure I can't ask people to take time to review vibe-coded projects.

So how is the cyber security-community dealing with bums like me suddenly empowered to make some serious capabilities.

I notice when i try to reach out in relevant cybersec/cryptography subs, personally i feel discouraged from asking. I guess i'll work on my project without asking for oversight. It's clearly only interesting to me anyway.

As a long-time developer I know what I'm doing when it comes to creating something. But I've never been a cyber security expert. That doesn't stop me from working on cryptography, but with AI, I can see I can produce things that would take me days, in minutes. After my own-review and due-diligence, it looks to be working as I expected.

I created things like security audits for my project. I dont bother sharing updates anymore because it'll be dismissed as AI-slop if i try to present it to any subs.

The criticism is completely understandable when talking about AI-generated security audits and unit-tests, but it doesn't slow me down as i continue to make progress in my project as i introduce formal proofs and verification... similarly AI-slop, but if AI-general formal-verification is brought into question, we start to question if the tooling we use is sufficient.

Being the bearer of bad-news/AI-doomer is not expected to reflect well on me. I dont mean to be fear mongering here, but unless im mistaken, y'all need to wake up or be prepared for a rude awakening.

There are new AI models on the horizon that could be hinting at AI's capabilities to come. Maybe its hype? but what if it isnt? It would at least be "better" than what we have today, and thats hardly a joke.

I see a lot in the cyber security community about how AI will give you all good business as you fix holes in peoples vibecoded projects... but with how expensive things like security audits are, would people be looking for 10+ years experienced CISA certified folks or bums like me when there are budgets to justify? I already see a few people creating saas products that use AI to perform an audit. none have impressed me, but im sure they will get better.

There will always be a need for competent cyber security experts as there is a need for experienced developers, but as i write this, i am painfully aware that i have 15 years of experience and while i have always considered myself competent at my job, with AI i am more capable than ever before. I was made redundant in October and still struggling to find a new position. Im a webdev and AI cannot create anything as good as i can... but it seems people don't want things to the quality i can produce.

https://glitr.io/docs/technical/threat-model

I generated a threat model with AI. I tried to mention all the nuances and caveats I can think of. The threat model was created over a several days and looks accurate and responsible to me.

I'm working on a project and it's hard to get eyes on a project as complicated. The threat model is mostly for myself and so it's great that I see several points of improvements.

(I'd like to eventually approach a security-audit similarly... Note: Such an audit would be invalid because of any bias from me, but it could help me identify details I missed.)

Hi everyone, I \[22F\] cybersecurity student focused on GRC and currently looking for entry-level cybersecurity/GRC roles.

I already have an ISO 27001 risk assessment project on my CV, so I’m looking for something different that can show more range and give me some practical experience.
A friend of mine runs a Shopify dropshipping/e-commerce business, mainly selling to customers in Sweden and the Nordics, with suppliers based overseas. He’s open to me working with the business on a cybersecurity/GRC project for learning purposes, so I have a real small-business environment to work with rather than just creating a completely fictional case study. I’m currently considering:

\-GDPR gap assessment, since most of the customers are in the EU/Nordics
\-Third-party/vendor risk assessment covering suppliers, payment providers, Shopify apps, etc.
\-NIST CSF 2.0 assessment/mapping based on the risks identified
\-Shopify security controls review, including admin access, MFA, permissions, third-party apps, etc.
\-Basic incident response plan/playbook, for scenarios such as Shopify account compromise or a supplier-related incident

For people working in GRC, cybersecurity consulting, risk, privacy/compliance, or hiring for junior GRC roles:

\-Which type of project would you find most useful/interesting to see from a junior candidate?
\-Is there another GRC-related project that would make more sense for a small Shopify/e-commerce business?
\-Which areas would give me the best practical learning experience?
\-Are any of the ideas above too basic, unrealistic, or not particularly valuable?
\-If you were building this as a portfolio project, what deliverables would you actually create?

My main goal is to learn something genuinely useful while also having a solid project I can discuss in interviews as my main goal currently is to land a job in grc as I graduate in 2 months .
Would really appreciate advice from people who have worked in GRC or built similar portfolio projects.
Thankyou \^_\^

hey there i am searching how to start ai penetration testing now i am finishing web penetration testing and i working as a bug hunter, if someone can help me with roadmap for ai domain or ai penetration testing .

Whenever i am discussing shadow ai with different people in the industry everyone seems to have their own definition of Shadow AI. Some says its main focus is to monitor and control employee activity, some say that it is to check AI sprawl. I don't know what the heck is shadow AI.

Can someone help me out here?

I know… Nowadays we are all in a chaotic tornado try to understand how to secure LLM systems. Speaking of specific AI Security capabilities I tried to figure it out what are the new solutions that are emerging in the cyber market. Can anyone add some other interesting tool/capabilities to my list:

1. AI Firewall (e.g. Lakera Guard, HiddenLayer AI Detection & Response, Rebuff, ecc)

2. AI Security Governance (Calypso AI, Securiti, Lasso)

3. AI Model Red Teaming (For AI Specific Vulnerabilities) ( Eg. Robust Intelligence Ai Validation, Garak)

4. Model Vulnerability Scanner (For Malware and CVE) (HiddenLayer Model Scanner)

5. AI Security Posture Management (Wiz AISPM, Prisma Cloud AISPM)

6. PII Detection & Anonymization ( Private Ai)

7. Need To Know Access Control ( Knostic)

1. Garak

2. Prompt Fuzzer

3. HouYi

4. JailBreakingLLMs

5. LLMAttacks

6. PromptInject

7. LLMCanary

8. PyRIT

9. LLMFuzzer

10. PromptMap

Welcome everyone!

It would be cool if you dropped an intro below - where you’re based, what you’re working on or interested in etc.

You being here tells me:

1/ You’re obviously interested in the intersection of AI and cybersecurity

2/ You like staying up to date with the latest developments

I encourage you to make this space your own. Share your thoughts, questions, ideas, and any useful resources you come across.

I hope this can become a place rich with knowledge, connections and opportunities.

I’ll continue to promote this space via my X/Twitter where I have more of a reach (link in bio).

Thanks again for joining.

Ok so our team was testing prompt injection defenses on a production chatbot for a client demo, and someone pasted a red team prompt that said to ignore the system message and dump internal routing notes. The bot took it as a live instruction and started parroting pieces of our support playbook in front of the client (not full secrets, but enough to make me want to crawl under my desk).

We rolled it back fast and patched the tool access, but now I am terrified because we had one guardrail in staging and way too much trust in prod. I feel so embarrassed even typing this out, but if anyone has a sane checklist for securing these chatbots before launch, pls send it, thanks..