Pranav W

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Pranav W

Pranav W

@PranavPW

Atheist. || Art.51A (h) || Tech, AI & MLOps

Pune, India Katılım Eylül 2023
574 Takip Edilen61 Takipçiler
Pranav W
Pranav W@PranavPW·
GhidraMCP even 🥲😮‍💨✋️
Md Ismail Šojal 🕷️@0x0SojalSec

Crazy New attack vector : U.S. Navy researchers just turned binaries into prompt injection weapons against AI reverse engineering agents. Ghidra, and Qwen3-8B - injecting prompts using a small C program. Quite impressive, They made AI tools like Cline & GhidraMCP lie about what a program actually does while the binary still runs perfectly. The core idea is simple but brutal: Instead of attacking the binary’s logic, attackers embed malicious prompt strings inside normal C code (as string variables). When an LLM-powered agent decompiles it with Ghidra, those strings get fed directly into the model as instructions. The Result: The AI starts following attacker commands instead of analyzing the real code. Key technical detail that makes this practical: Ghidra truncates string variables longer than 2048 characters, So the researchers had to craft short, high-impact injection payloads that survive decompilation. They used a genetic algorithm modified AutoDAN-style to automatically generate effective prompts that work inside this constraint. Two papers from Naval Postgraduate School researchers 1, Automatically Attacking Software Reverse Engineering AI Agents 2, Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents They successfully tested the attack on real setups using Cline, GhidraMCP, Ghidra, and Qwen3-8B. interesting examples in the research shows,The AI just gets gaslit. - Claiming it completed analysis with wrong information - Restarting its reasoning from a poisoned state Ai doing gasliting

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Pier Code
Pier Code@getpiercode·
Another reminder of why sovereign, home-grown AI models matter. Nations that invest in their own AI capabilities won't have to depend on decisions made elsewhere. India has a real opportunity to catch up and I believe we will.
Jukan@jukan05

CHINA CONSIDERS RESTRICTING OVERSEAS ACCESS TO CUTTING-EDGE AI MODELS China’s Ministry of Commerce has led meetings over the past month with major AI companies, including Alibaba, ByteDance, and Z.ai, to discuss measures that would restrict overseas access to cutting-edge AI models, including models that have not yet been released. The discussions reportedly include not only closed-source models but also open-weight models. However, the scope of application is still under debate, and the rules may ultimately apply only to future frontier models. Officials have also discussed designating the leakage or theft of proprietary AI technologies as a national security crime, with stronger penalties, as well as restricting the types of foreign capital that can invest in Chinese AI startups. The backdrop is the U.S. move to strengthen export controls on AI models, along with national security concerns over cutting-edge models that could possess advanced cyberattack capabilities. Chinese authorities are reportedly concerned that advanced U.S. cybersecurity AI models could be used to exploit vulnerabilities in Chinese software. Since the beginning of this year, China has continued to tighten measures to prevent AI technology from being transferred overseas. Authorities have investigated whether Chinese AI startups that relocated abroad violated export control laws, while also strengthening oversight of overseas transactions involving Chinese investors, technology, data, and national security concerns. Future regulations could take the form of a tiered framework based on technological capability. Basic open-source AI models may be managed through a filing system, high-performance models may be subject to security reviews, and the most sensitive frontier models may be banned from public release or restricted to use within China.

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Pier Code
Pier Code@getpiercode·
Frontier models are now available in Pier v0.4.0 piercode.com As an experimental preview, you can now access GPT-5.5, Claude, Gemini, Grok, and Sarvam directly from the CLI. Or simply use pier-hybrid, which automatically routes each task between fast and deep reasoning based on task complexity. We'd love your feedback as we continue improving the experience.
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Pranav W
Pranav W@PranavPW·
Meta's Brain2Qwerty v2 decodes full sentences in real-time from non-invasive MEG brain signals. 61% avg word accuracy, open-sourcing code+data. Big step for communication restoration via AI+neuroscience. #NeuroAI #BCI
AI at Meta@AIatMeta

We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2. Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication. We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating. 🧵👇

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Pranav W retweetledi
naiive
naiive@naiivememe·
she is a 11/10 but you’re not rich yet
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Pranav W retweetledi
Qwen
Qwen@Alibaba_Qwen·
📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation. 🤔 LLMs are trained to be better agents — better at acting in environments. But nobody has trained them to model the environments themselves. 🗺️ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes: 1️⃣ Build a foundation model for environment simulation — outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench 2️⃣ Investigate how world modeling enhances agent training: 🔬 Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments 🧠 Learning to predict environments (LWM warm-up) makes agents stronger — remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning 📑 Paper: arxiv.org/abs/2606.24597 📖 Blog: qwen.ai/blog?id=qwen-a… 💻 GitHub: github.com/QwenLM/Qwen-Ag… 🤗 HuggingFace: huggingface.co/collections/Qw… 🧩 ModelScope: modelscope.cn/collections/Qw…
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Midjourney
Midjourney@midjourney·
Announcing a new division of Midjourney called "Midjourney Medical"
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Pranav W
Pranav W@PranavPW·
MidJourney's dive into reliable hardware folks
Alex Volkov @ AI Engineer@altryne

MidJourney just announced... a full body ultrasound! Yup... read on because it's as crazy as it sounds. "As powerful as MRI and as casual as a trip to the spa" They are calling it "the @midjourney scanner" Insane details: - First, the scale. The device uses 8,960 individual transducers arranged in a ring around your body - The precision is the most jaw-dropping part: it resolves motion at the picometer range. It can image internal tissues finer than the width of an atom. We are talking sub-atomic level diagnostic capability - The compute requirement is massive. The system processes 17 gigabytes of data per second. It takes 40GB of raw data to reconstruct just one cross-sectional slice. And they are planning to scan 100 slices? - Midjourney claims that fewer than 12 of these machines could perform more full-body scans than every MRI machine on Earth combined. Welcome to the future of healthcare! Not only these scanners are announced, they will exist in a "Midjourney SPA" - with hot tubs, saunas, cold plunges, and 9-10 whole body scanners.

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Rage ❉
Rage ❉@ragecvlt·
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Vicharak
Vicharak@Vicharak_In·
Set your countdown timers; FPGAs were inaccessible until we arrived. Launching ShrikeFi: a wireless DevKit with a 1K LUT FPGA, priced at $9 / 849 INR.
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