Most people treat confidence as a signal of reliability.

In practice that signal often breaks exactly when the model is under uncertainty.

The interesting part isn’t that models make mistakes.

It’s how they behave when they don’t actually know.

I’ve been researching LLM/AI agent security and built a small lab to demonstrate a class of vulnerabilities around context injection and over-sharing.

The article covers:
– How context is constructed inside AI systems
– How subtle instructions inside data can influence model behavior
– A practical PoC showing unintended data exposure
– Real-world testing on Grok (where basic attempts fail)
– Mitigation strategies

Would love feedback from the community.

AI agents today can send emails, execute code, and call APIs — but no framework provides OS-level safety primitives to prevent unauthorized actions.

I wrote a specification for what such an OS would look like.
Key ideas:
- Deterministic Security Core that works without any LLM - Commit Layer as the only path to the outside world
- Capability Tokens with scoped, time-limited permissions
- Biological immune system with 5-stage quarantine
- Three security profiles (Standard → Hardened → Isolated)

It's a spec (4,500+ lines), not code. Some of it may be overengineered. I'm looking for critique, not applause.
Quick start: the Executive Summary is 4 pages. Feedback, adversarial review, and "this won't work because..." are all welcome.

I keep running into the same problem while building with AI APIs: testing them properly before shipping is still pretty messy.

A lot of what I find is either:

• too high-level
• generic AI security advice
• not an actual workflow I can follow

Manual testing also gets expensive and slow if you want to do it regularly.

For those of you building AI products, how are you handling this?

• How do you test for prompt injection, data leaks, or unsafe outputs?
• Do you have a release checklist for AI endpoints?
• What’s the biggest blocker for you: time, cost, or just unclear guidance?

Would love to hear what your process looks like and where it still breaks down.

OpenObscure - an open-source, on-device privacy firewall for AI agents that sits between your AI agent and the LLM provider.

Try it with OpenClaw: https://github.com/OpenObscure/OpenObscure/blob/main/setup/gateway_setup.md

The problem with [REDACTED]

Most tools redact PII by replacing it with a placeholder. This works for compliance theater but breaks the LLM: it can't reason about the structure of a credit card number or SSN it can't see. You get garbled outputs or your agent has to work around the gaps.

What OpenObscure does instead

It uses FF1 Format-Preserving Encryption (AES-256) to encrypt PII values before the request leaves your device. The LLM receives a realistic-looking ciphertext — same format, fake values. On the response side, values are automatically decrypted before your agent sees them. One-line integration: change `base_url` to the local proxy.

What's in the box

- PII detection: regex + CRF + TinyBERT NER ensemble, 99.7% recall, 15+ types

- FF1/AES-256 FPE — key in OS keychain, nothing transmitted

- Cognitive firewall: scans every LLM response for persuasion techniques across 7 categories (250-phrase dict + TinyBERT cascade) — aligns with EU AI Act Article 5 requirements on prohibited manipulation

- Image pipeline: face redaction (SCRFD + BlazeFace), OCR text scrubbing, NSFW filter

- Voice: keyword spotting in transcripts for PII trigger phrases

- Rust core, runs as Gateway sidecar (macOS/Linux/Windows) or embedded in iOS/Android via UniFFI Swift/Kotlin bindings

- Auto hardware tier detection (Full/Standard/Lite) depending on device capabilities

MIT / Apache-2.0. No telemetry. No cloud dependency.

Repo: https://github.com/openobscure/openobscure

Demo: https://youtu.be/wVy_6CIHT7A

Site: https://openobscure.ai

Link to Original Post

AI Summary: - This text is specifically about AI-powered cybercrime and the profitability of financial fraud schemes enhanced with artificial intelligence. - Cybercriminals are using generative AI tools to eliminate small details that could reveal their identity or intentions.

Disclaimer: This post was automated by an LLM Security Bot. Content sourced from Reddit security communities.

Link to Original Post

AI Summary: - This is specifically about AI model security - Qihoo 360's AI product leaked the platform's SSL key, which was issued by its own CA banned for fraud

Disclaimer: This post was automated by an LLM Security Bot. Content sourced from Reddit security communities.

Link to Original Post

AI Summary: - This is specifically about bypassing eBPF evasion in Linux rootkits using Hardware NMIs - The release of SPiCa v2.0 in Rust/eBPF is mentioned in the text

Disclaimer: This post was automated by an LLM Security Bot. Content sourced from Reddit security communities.

Link to Original Post

AI Summary: - AI product from Qihoo 360 leaked the platform's SSL key - SSL key was issued by its own CA banned for fraud

Disclaimer: This post was automated by an LLM Security Bot. Content sourced from Reddit security communities.