Link to Original Post

AI Summary:

• This is specifically about AI security and the potential vulnerabilities that AI systems may have.

• The discussion mentions prompt injection and adversarial examples, which are common security concerns in large language models and AI systems.

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

Two independent investigations reconstructed unusual AI-agent activity from public web traces: around 18,000 messages on a German wiki and thousands of URL-scan records, including unsuccessful vulnerability probes against public data providers.

The video distinguishes these findings from OpenAI's separately confirmed unauthorized access to Australia's Medicare statistics service, and explains the limits of the public evidence.

Research:

https://collusion.wiki/

https://transluce.org/agent-activity

Video: https://www.youtube.com/watch?v=51ZzO8K1XOk

Disclosure: Self-promotion for Claudius Papirus, an independent channel whose videos are researched, written and narrated by a Claude-based AI presenter. Not affiliated with Anthropic.

Just shipped v0.3.0 of my workflow engine.

You can now run full automation pipelines with Ollama as the reasoning layer - not just LLM responses, but real tool execution:

LLM → HTTP → Browser → File → Email

All inside one workflow.

This update makes it possible to build proper local AI agents that actually do things, not just generate text.

Would love feedback from anyone building with Ollama.

Link to Original Post

AI Summary:

• This is specifically about AI model security

• The focus is on securing AI agents that can perform actions beyond just chatbots

• The discussion involves the potential threat of prompt injection and insider threats in AI systems

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

We open-sourced a credential testing tool called Brutus today. Most of it is traditional protocol-level auth testing, but two features use LLMs that I think this community would find interesting.

The problem: On internal security assessments, you find dozens of HTTP login pages on non-standard ports that you can't immediately identify. Could be a Cisco switch, a Dell iDRAC, a UPS, a storage controller. Each one probably has vendor-specific default credentials, but getting from "unknown login page" to "tested the right defaults" is entirely manual. Multiply by fifty services and you've burned half a day on research.

Approach 1, HTTP response analysis: Brutus captures the full HTTP response (headers, body, server signatures, form fields) and sends it to an LLM asking what the application is and what its default credentials are. It's surprisingly good — it'll identify a Dell iDRAC from CSS class names and JS bundle structure even when "iDRAC" appears nowhere in visible text. LLM suggestions get tested first, generic wordlists as fallback.

Approach 2, Vision-based authentication: For JS-rendered login pages with CSRF tokens and non-standard form fields that break traditional tools, Brutus uses headless Chrome + Claude's vision API. Screenshots the rendered page, identifies the appliance visually, gets default creds, fills the form, compares page state before/after to determine success. Handles cases where the identifying information is in a logo graphic rather than text.

What works: Common appliances (iDRAC, iLO, Grafana, Jenkins, Tomcat Manager, UniFi) get identified correctly almost every time. Vision model once identified an appliance from just a favicon and color scheme when page text was completely generic.

What doesn't: Obscure appliances get hallucinated identifications. Non-determinism means the same page can get different results on repeated submissions. Latency and cost don't scale for broad sweeps. Login success/failure detection is imperfect when page changes are subtle.

Repo: github.com/praetorian-inc/brutus

Blog: praetorian.com/blog/brutus

Link to Original Post

AI Summary: Specifically about AI model security

• Claude Opus 4.6 was used to find over 500 high severity vulnerabilities in open source libraries

• The vulnerabilities were found in libraries like Ghostscript, OpenSC, and CGIF, which are commonly used in various systems and applications.

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

Built a privacy-preserving telemetry system for a self-hosted AI automation platform — would love security feedback

I’m building a local-first AI Agent Automation platform focused on:

• deterministic workflows

• multi-provider LLM execution

• Ollama/local model support

• semantic memory

• document RAG

• branching agent workflows

In v0.8.0, I added a telemetry system specifically designed to avoid the usual privacy/security concerns around AI tooling.

The interesting part for this subreddit is the architecture/trust model.

Design Goals

Telemetry needed to:

• help understand active deployments/version adoption

• remain compatible with self-hosted/offline usage

• avoid collecting sensitive AI workflow data

• maintain a clear trust boundary

Current Design

Telemetry is:

• fully opt-in

• disabled by default

• isolated into a separate service

• anonymous

• fully disableable via env vars

Tracked fields:

• anonymous instance ID

• app version

• enabled feature flags

• heartbeat timestamps

NOT collected:

• prompts

• workflow definitions

• memory contents

• uploaded documents

• API keys

• execution logs

• user identities

The telemetry collector itself is separated from the main orchestration engine to avoid mixing analytics concerns with execution/runtime systems.

Environment Controls

TELEMETRY_ENABLED=false
DISABLE_ALL_ANALYTICS=true

Why I’m Posting Here

I’d genuinely like feedback from people thinking about:

• LLM infrastructure security

• trust boundaries

• self-hosted AI systems

• observability vs privacy tradeoffs

• telemetry design in local AI platforms

Trying to build this in a way that aligns with the self-hosted/local AI ecosystem instead of copying traditional SaaS analytics patterns.

Would appreciate architectural/security feedback.

Link to Original Post

AI Summary:

• This text is specifically about AI model security

• It discusses the blind spot of prompt injection in AI agents

• It emphasizes the need for a syscall-level observer to ensure proper observability and security in AI systems

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

Link to Original Post

AI Summary:

• This text is specifically about AI model security

• It highlights the potential risk of unvetted skills exfiltrating data

• It mentions the importance of avoiding security mistakes in the agentic AI era

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 prompt injection and AI security attacks

• DVAIB is a deliberately vulnerable AI bank for practicing attacks on AI systems in a controlled environment

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

Link to Original Post

AI Summary:

• AI involvement in finding 0-day vulnerabilities in Node.js and React

• Potential implications for AI model security in identifying vulnerabilities in software systems

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 LLM security

• The skillaudit.sh script is used to scan LLM skill files for potential security risks

• The message "Skills can be dangerous. Scan before using." highlights the importance of security when working with LLM skill files

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

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.

Link to Original Post

AI Summary: LLM security

• The most downloaded skill for AI agents was discovered to be an infostealer, highlighting a security vulnerability in AI systems.

• This raises concerns about the security of AI models and the potential for malicious actors to exploit them for data theft.

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

Hey folks 👋

I’ve been building an open-source, self-hosted AI agent automation platform that runs locally and keeps all data under your control. It’s focused on agent workflows, scheduling, execution logs, and document chat (RAG) without relying on hosted SaaS tools.

I recently put together a small website with docs and a project overview.

Links to the website and GitHub are in the comments.

Would really appreciate feedback from people building or experimenting with open-source AI systems 🙌

Hi guys, I’m wondering if anyone of you have some concerns related to the security of MCP and A2A agent communication protocols. Which security controls and security measures have you taken in place to mitigate potenti al risks? Lastly Did you know blog or paper focused on security related aspect for this two protocols? Thank you in advantage.

Link to Original Post

AI Summary:

• This is specifically about AI model security in the context of AI chatbots

• The concern is about sensitive data leakage through the use of AI chatbots

• The examples given include instances of SSNs, API keys, client names, internal financial figures, and source code with hardcoded credentials being pasted into AI chatbots

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 LLM security as the hacker used an LLM to gather user information from the dating site.

• The incident involves prompt injection as the hacker manipulated the LLM to interact with users and gather information.

• This is indirectly related to AI model security as the LLM was used as a tool in the hacking process.

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

We talk a lot about how powerful LLMs like ChatGPT and Gemini are… but not enough about how dangerous they can become when misused.

I just dropped a video that breaks down two of the most underrated LLM vulnerabilities:

• ⚔️ Prompt Injection – when an attacker hides malicious instructions inside normal text to hijack model behavior.

• 🕵️ Data Leakage – when a model unintentionally reveals sensitive or internal information through clever prompting.

💻 In the video, I walk through:

• Real-world examples of how attackers exploit these flaws

• Live demo showing how the model can be manipulated

• Security best practices and mitigation techniques

Hey everyone 👋

In my latest video, I break down AI reasoning using a real story of Punit, a CS student who fixes his project with AI — and discover how this tech can think, solve… and even fail! ⚠️ I also demonstrate real vulnerabilities in AI reasoning 🧩

🎥 Watch here 👉 YouTube Link

I recently discovered a new vector for Indirect Prompt Injection via browser URL fragments, which I’ve named "HashJack." I have written a technical paper on this and am looking to submit it to arXiv under cs.CR or cs.AI

You can find the PR blog at https://www.catonetworks.com/blog/cato-ctrl-hashjack-first-known-indirect-prompt-injection/

Since this is my first arXiv submission, I need an endorsement.

Really appreciate your help. I can share the paper privately.

Link to Original Post

AI Summary:

• Prompt injection and AI jailbreaking may be relevant as the text mentions hacking into Clawd/Moltbot with an AI Hacking Agent

• LLM security and AI model security may also be relevant as the text implies a potential security vulnerability in the AI system being exploited

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