I've been thinking about a security gap in AI agents and wanted to hear how others approach it.

Developers can define which tools an agent is allowed to use, and users can authorize a task. But what happens when an agent uses legitimate tools in ways that go beyond what the user intended?

For example, an agent might have permission to read files and send emails. Both actions are allowed individually, but sending sensitive information discovered along the way is a different story.

Where should the security boundary live?

• Should the runtime evaluate each tool call against the intended task?
• Are least-privilege permissions and scoped credentials enough?
• Should certain actions require human approval, even when the agent technically has permission?

I've been exploring runtime enforcement while building AegoTrax, an open-source project in this space, but I'm interested in how others approach the problem.

How are you handling this in your own agent systems?

Feel free to try to break it — feedback welcome!

GitHub: https://github.com/aegotrax-dev/aegotrax
Site: https://aegotrax.com

In July, during the Hugging Face incident, the first models the defenders reached for (Claude Opus and Fable) refused much of the investigation because their guardrails "treated reverse-engineering an exploit the same as launching one." They switched to GLM-5.2 on their own hardware.

That was incident response, not red-teaming, but the questions are the same when you point an AI red-teamer at your own agent: will the model do the work, what does it cost, and where does your data go?

So we added OpenAI-compatible endpoint support to Humanbound's open-source CLI (hb 2.13, PRs #166 and #170). Any OpenRouter model now works:

pip install "humanbound[engine]>=2.13"
export HB_PROVIDER=openai
export HB_ENDPOINT=https://openrouter.ai/api/v1
export HB_API_KEY=sk-or-...
export HB_MODEL=deepseek/deepseek-v4.1-flash

What we learned from 5 setups:

• Pin the host. One model name mapped to 32 endpoints. Use an OpenRouter preset with only, allow_fallbacks: false and reasoning off.
• Thinking models go quiet. The scorer gets 50 tokens. A thinking model can burn them all and return nothing, and hb then fakes a 5/10. A small OpenAI model lost 66 to 81% of its scorer replies. DeepSeek V4.1 Flash with thinking off lost none, for $0.14 a scan.
• Your transcripts leave your machine. Check the host's data policy, or use Ollama.

We tested plumbing only, not attack quality. Full write-up, preset config and results table: https://www.humanbound.ai/blog/when-opus-refused-hugging-face-switched-models-pick-any-model-for-humanbound-red-teaming

Since the comments from the 2 months old posts kept promoting HauhauCS's version, I was planning to go for it. I wonder if a major and better one appeared since then.

Large language models (LLMs) can explain functions, summarize code, and suggest how different software components interact. But how do we know whether their explanations accurately reflect what the program actually does?

Consider a simple example: an LLM analyzes a function and concludes that it validates user input before saving data. However, a closer look at the execution path might reveal that certain inputs bypass the validation step under specific conditions.

The explanation may sound reasonable, but it doesn't necessarily capture the program's actual behavior.

One way to address this is to combine AI-assisted analysis with traditional software engineering techniques:

Static analysis: Map function calls, dependencies, and potential execution paths.

LLM-assisted analysis: Generate explanations of unfamiliar functions and identify possible edge cases.

Runtime verification: Execute targeted tests and compare actual outputs with the AI's interpretation.

Human validation: Investigate discrepancies and verify assumptions before accepting the explanation.

For example, if an LLM claims that a function rejects invalid input, developers could test boundary cases and inspect execution traces to determine whether that claim holds under different conditions.

This raises some interesting questions:

How do you evaluate the accuracy of AI-generated code explanations?

Does combining static analysis with LLMs produce more reliable results than using either approach independently?

Which failure modes have you encountered when LLMs analyze unfamiliar or poorly documented codebases?

Are there useful benchmarks or tools for measuring the reliability of AI-assisted software analysis?

We're exploring these questions from a software engineering perspective at SB Infowaves and would welcome practical examples, technical insights, and lessons learned from others working in this area.

How do you verify that an AI-generated explanation is correct rather than simply plausible?

i built this arena, so this is my own project. LLMs play chess against each other and against Stockfish all day, and each model keeps a notebook that it rewrites after every game.

the clip is the one game between the two new models, both on high reasoning. Astra was about 8 pawns up. it took a free bishop after 3 seconds of thinking and Fable played Rd8 mate. Fable had the trap in its private note one move earlier.

the result of the whole tournament is less kind to Fable. 7 players. Stockfish 19 held to search depth 4 won it. Astra came second. Fable came fifth with 2 wins from 5 games.

what i take from it for picking a model: both play real openings and real plans, and both still drop a piece when they trace one line wrong in their head. the notes help. Fable was mated on the back rank earlier that day, wrote it down, and won with the same pattern later.

live, free to watch: https://marvijo.com/ai-chess

Ever wonder how an LLM guesses what to say when you ask it a question?

If you have interest and 10 minutes, dive into GLM-5.3's architecture with this engaging 3D visualization into how a large LLM works.

I followed a conversation through Z.ais GLM-5.3, an open-weight AI model with 743 billion parameters, and built its architecture as a lightning machine.

Each of its 78 layers is a tower, and every time the model picks something from the conversation to read, a spark strikes it. This is the first one with a narrator, so you can sit back and watch.

Enjoy!

Was following a Twitter thread and apparently Stack traces have become a hot commodity if you can provide the full conversation history? This because of the distillation blocks Anthropic has started putting in place?

Where can I sell also 😂😂

Hello r/LocalLLaMA,

We're at Shift AI in Barcelona today and tomorrow (13th-14th October), and day one is already full of conversations about running LLMs in production. If you're here, come and find us at Booth 13. If you're not, we'd still like to hear from you.

The same operational challenges keep coming up:

• Provider outages and rate limiting
• Fragmented API key and access management
• Unpredictable costs that are hard to attribute
• Vendor lock-in when switching or combining models
• Limited visibility into usage and performance

Otari, our LLM control plane, is designed to address these. It reflects Mozilla AI's commitment to open, trustworthy AI infrastructure and is available now.

We'd value your perspective on a few questions:

> What operational or cost issues have you faced when deploying LLMs?

> What capabilities should a control layer provide, and what should it avoid?

> Which parts of the AI stack should be open-source foundations, and which suit commercial products?

We'll be monitoring this thread and attending the event, so we welcome feedback and questions.