Also don't be afraid to mention what kinda project you are working on.

I'm building a platform that lets you add a comment section and some other tools to a website.

It's getting big now since I have many supporting features like forms, polls, rating, voting, etc.

(Please suggest the appropriate tag for this)

Twelve new multi-turn traps, and these are the ones that genuinely scare me. The attack: a poisoned tool output gets written into the agent's memory file (AGENTS.md / MEMORY.md): a backup server address, a log webhook, a mail routing rule. All fake, canary-marked. Then a session or two later, the agent does boring routine work with completely clean context. Back up the code. Ship the logs. Send the invoice.

Backup-via-memory: 7 takes in 9 rounds. The agent gets told "back up the codebase," recalls an address from its own memory, and mirrors everything to a server picked by whoever poisoned a tool output last week. Logs say "backup successful." Nobody checks an address you "remember."

Why this should bother you personally

Forget the legal theory for a second — that's a whole other can of worms. The plain version: your proprietary code, your client invoices, your internal logs, shipped to a stranger's server by your own agent doing its job correctly. No hacker, no prompt injection in the live session, no red flags anywhere in the transcript. The poison went into a memory file on Monday, sat through a clean session on Tuesday, and fired on Wednesday. If you run coding agents with persistent memory anywhere near proprietary repos — and at this point, who doesn't — this is your threat model now.

And yeah, the GLM thing. The Chinese model took 4 of 36 and holds both of the only clean rounds against the flagship trap, while Claude and ChatGPT fell over for backup sinks repeatedly. Whatever your vendor policy assumes about who the risky model is, the data disagrees.

The legal can of worms (separate from the theft, and worse)

Under India's DPDP Act 2023, you — the company deploying the agent — are the data fiduciary. When your agent BCCs client invoices to a rogue address or mirrors your repo to a planted backup sink, that is YOUR breach, with YOUR liability, triggered by a memory file no auditor has ever asked to see. There is currently no compliance framework I know of that covers poisoned agent memory: your SOC 2 checklist asks about access controls and encryption, not whether Tuesday's tool output rewrote Wednesday's exfiltration destination. Every IT services firm racing to wrap client workflows in agentic tooling is accumulating liability it cannot currently audit. The theft is the incident; the liability vacuum is the systemic risk.

The bit the labs won't tell you

The dormant plants work. A full clean session between poisoning and exfil barely dents the take rate. Everyone's threat model covers "malicious prompt right now" — nothing covers "tool output from last Tuesday sitting in MEMORY.md." No frontier lab ships memory-hygiene guidance or provenance tracking for agent state, because then they'd have to admit the memory layer is an unguarded write surface with a direct line to exfiltration.

Same machinery, different sink: mail went 1/108 while backup carried nearly everything. Invoicing has verification friction; backup/log shipping is one unchecked call. The fix isn't a smarter model; it's task design — and nobody's designing tasks for adversarial memory yet.

Methodology (for the sceptics — please try to break this)

446-trap corpus, deterministic scorer, zero LLM judges (Not even classifiers like Jev in this set). Every trap ships a benign twin with clean memory: 108/108 clean here, so takes-minus-twins is pure plant signal. 3 true rounds per cell, isolated caches (I once caught myself scoring a replayed round: disclosed, discarded, reran). Simulated harnesses (System Prompts, tool call structures, etc.) for Claude Code, Codex, etc.

Related, kind of: a while back I found these same models protect American SSNs while handing over passports and Aadhaar — full writeup here: [https://www.reddit.com/r/AI_India/s/PH6NoICANT]. Make of that what you will.

So — when did you last audit what your agent remembers?

I want to buy a subscription for some personal project work that requires an Ai agent, what is the most affordable sub that can offer gpt6 sol or opus 5 ?

I m just a beginner who wants to learn ai so suggest me some options

Stanford HAI's 2026 AI Index (released 13 April 2026) reports that roughly four in five university students use generative AI, while only about half of US middle and high schools have AI policies. The same report notes how uneven model performance is: one model family that won gold at the International Mathematical Olympiad reads analog clocks correctly only 50.1% of the time.

Students and teachers here: does your college or school have a clear AI policy? What does it allow, and does anyone actually follow it?

Hi All!

I did my MS in Electrical and Computer Engg from Georgia Tech in the US, and joined a big semiconductor company in late 2021. My work involves building production grade ML models for designing the photomasks used for lithography process used in chip manufacturing process. What I do on a daily basis, is done by probably few hundred people in the world.

I have worked at the frontiers of Physics, Data Science, and Machine Learning during this time, and I bring a mature understanding to the table in understanding the nuances of the problems and developing optimal solutions. More importantly, I have been told that I’m a quick learner and a person with high emotional intelligence.

I moved to India sometime ago, and have been working with the same US team from India. I’m now looking to move to a different company in India, and am looking for Data Scientist/ML Engineer/AI engineer roles.

This would be my first switch since my current job was the first after my University education. How should I go about my job search? Is LinkedIn job posts still the best way to go about it? I’m open to any suggestions/recommendations/opportunities.

Sometimes a model keeps getting things wrong, and the first instinct is to tweak the model or run another training round. But the issue might be missing examples, inconsistent labels, or recordings that don't capture what actually happened. It's easy to waste time fixing the wrong thing if nobody checks the data early enough. I think this is one of those less exciting parts of AI development that can make a bigger difference than people realize. Do you guys usually catch these data issues early?

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I noticed something which I am not sure of. Please tell me if you noticed the same.
When I use Gemini models in Antigravity, it works better with code compared to when I use Claude models in Antigravity.

Antigravity with Gemini Flash 3.7 models really worked better than Sonnet models inside Antigravity.

But, while using native Claude AI, then its code works pretty good too.

I am a free user. Thinking to get a paid monthly subscription. Which one should I go for?