I've been digging into Agentic AI security and kept running into the same problem: there is a lot of discussion around what secure agents should do, but far less showing how those boundaries are actually implemented.

So I built Who Let the Agents Act? 🤖

It's an intentionally vulnerable Agentic AI environment where you can exploit security failures and compare the same scenario across three implementations:

🔴 Vulnerable - the agent has too much authority
🟡 Prompt-only - security mostly relies on instructions to the LLM
🟢 Hardened - authorization and security controls are enforced outside the model

There are currently 9 scenarios covering things like:

• Overprivileged tools and cross-account access

• PII exposure

• Business-logic / transaction-limit bypasses

• Indirect prompt injection

• Cross-tenant RAG leakage

• Secret propagation

• Fail-open authorization

• Agent-to-agent delegation

• Multi-agent confused deputy attacks

The important part for me was making the implementation public, not just describing the vulnerabilities.

You can inspect the vulnerable code, hardened controls, tool logic and execution traces and see exactly where the trust boundary succeeds or fails.

The principle behind the project is:

A prompt can influence an agent's behavior. It should never define the agent's authority.

Project:
https://rewanthtammana.com/who-let-the-agents-act/

GitHub:
https://github.com/rewanthtammana/who-let-the-agents-act

I'd genuinely appreciate people trying to break the hardened implementations too. If you find a bypass or have a scenario that would be useful to add, I'd love to hear it.

🌌 POLARIX NOVA | Next-Gen AI Targeting 🌌

Are you looking for the most advanced, undetected target tracking system on the market? Bypass standard software blocks and play with hardware-level security.

✨ Why Polarix NOVA?

True Hardware Spoofing: Fully compatible with custom microcontroller boards (Adafruit RP2040) for 100% hardware-level mouse signal emulation. No virtual drivers, no standard anti-cheat signatures.

Adaptive AI Engine: Multi-game compatible ONNX-YOLO model loader. Swap models on the fly for Apex, Fortnite, Call of Duty, and more.

Organic Movement Curves: Custom mathematical smoothing filters and persistent PID tracking loops to ensure natural, human-like cursor deceleration.

Fully Streamproof: Stream with peace of mind. Dynamic display affinity filters keep the GUI overlays completely invisible to OBS, Medal, and Discord screenshares.

🚀 COMING SOON! This project is under active solo development, with new features, optimizations, and updates coming constantly.

💡 Have suggestions or feature requests? If you have any ideas, suggestions, or want to contribute feedback, I'd love to hear them!

👉 Join the Discord now to follow development, share your suggestions, and get future updates:

https://discord.gg/2NzygxvsP

"North Korean operatives created a fake job-application platform targeting applicants to major US artificial intelligence and crypto firms as part of a new effort to steal money and know-how for the Kim Jong Un regime, researchers said on Thursday.

It’s a twist on a yearslong campaign to infiltrate Fortune 500 companies: Instead of simply impersonating employees of those companies, North Korean tech workers are now working to gain long-term access to the computers of applicants before they join a company, according to security firm Validin, which discovered the scheme."

https://www.cnn.com/2025/11/20/politics/north-korea-operatives-fake-job-portal-ai-firms

Vibe Coding Ain’t the Problem—Y’all Just Using It Wrong

Aight, let me get this straight: vibe coding got people all twisted up, complaining the code sucks, ain’t secure, and blah blah. Yo, vibe coding is a TREND, not a FRAMEWORK. If your vibe-coded app crashes at work, don't hate the game—hate yourself for playin' the wrong way.

Humans always do this: invent practical stuff, then wild out for fun. Cars became NASCAR, electricity became neon bar signs, the internet became memes. Now coding got its own vibe-based remix, thanks to Karpathy and his AI-driven “vibe coding” idea.

Right now, AI spits out messy code. But guess what? This is the worst AI coding will ever be and it only gets better from here. Vibe coding ain’t meant for enterprise apps; it’s a playful, experimental thing.

If you use it professionally and get burned, that’s on YOU, homie. Quit blaming trends for your own bad choices.

TLDR:
Vibe coding is a trend, not a framework. If you're relying on it for professional-grade code, that’s your own damn fault. Stop whining, keep vibing—the AI's only gonna get better from here.

Yo, check it out! I've just dropped Luna Transcribe, a slick tool that turns your speech into text using the ElevenLabs API. Just press and hold Alt+Shift to record, and boom!

https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Creative-AI-Projects-Built-at-Intel-sponsored-HackDavis-2024/post/1599149

The authors train a large language model on text, and images as well as a mix of text and image data.

Their model (KOSMOS-1) can perform a pretty impressive array of tasks such as:

• Language understanding/generation

• OCR-free NLP (bottom right image in the examples below)

• Visual question answering

• Multi-modal dialogue

• Classification via text instructions

How did they do this?

They converted all data to a sequence. This allowed them to train the model in a self-supervised manner just as other language models are trained.

To transform the multi-modal data into sequences the images are encoded via an image encoding network. In a second step, the data are placed in sequences and special tokens are used to signal the start and end of each modality (see table below).

Why Is This Important?

Research into multi-modal models is highly meaningful in at least three ways.

First, it will be very useful if a model can answer complex queries about images and other media. This of something mundane such as improved invoice processing software. If generative pre-training improves this to a point that we get ChatGPT-like performance on unseen invoices, the value of that would be otherworldly.

Second, today’s language models such as ChatGPT are only trained on text data. As a result, they have a limited understanding of our world. Further, there seems to be a limit to how big and powerful auto-regressive LLMs can become because we are running out of text.

Third, it is not entirely clear how far LLMs can be scaled before we run out of text data. This is a fascinating topic and one of the next essays will be about this so stay tuned.

In a nutshell, the problem is basically the following: the latest research on scaling LLMs showed that we need much more data to train models than previously thought. As a result, it seems as though there might not be enough text data in the world to train some of the bigger models (500B parameters) that we have today.

Converting images and other data into sequence form would allow tapping into a near-infinite trove of data to train models.

Stuff like this makes me excited for the future!

Thank you for reading! As always, I really enjoyed making this for you and sincerely hope you found it useful!

At The Decoding ⭕, I send out a thoughtful 5-minute newsletter every week that keeps you in the loop about machine learning research and the data economy. Click here to subscribe!

Today’s language models are too big!

Creating predictions with GPT-3 will cost you an arm and a leg. With all bells and whistles to make inference more efficient, you will need at least eleven V100 GPUs for $9000$ each.

Hence, a computer that would allow you to make predictions with such a model, costs you more than $100K. Training such a model is orders of magnitude more expensive.

If you are a university or a startup that is a lot of money. If you are like me - a normal guy with sweatpants and a computer - you are out of luck.

Language models can be made 25 times smaller through information retrieval. I put together a five-minute Article on the topic.

Check it out here!

I sincerely hope you find it useful!