We just cracked the AI jailbreak problem!. Our new adversarial guardrail slashes attack success from 82% → 6% while keeping latency under 85ms. How? .We built an AI attacker to constantly jailbreak our own defender. No more: .❌ Data leaks from prompt injections .❌ Agents

Why it matters? As agents scale into real-world use cases, so do their risks. ASI gives builders awareness & examples to secure agents before attackers exploit them. Explore & contribute here 👇.

Frameworks covered so far:.• LangChain.• LangGraph.• CrewAI.• AutoGen.• OpenAI Swarm (exp.).• Amazon Bedrock Agents.…and more coming soon.

OWASP repo contains deliberately insecure agent samples ⚠️. Each one demonstrates how misconfigurations or insecure code lead to real vulnerabilities. Think of it as a practical guide to what not to do when building with agents. More Details .

Agents are powerful, but insecure agents can be dangerous. @owasp's Agentic Security Initiative (ASI) shows how common agent frameworks expose risks mapped to the GenAI Top 10 (2025)

The most believable opinion is the Swarming AI adversaries, defending on-chain world.

One agent ≠ enough. QuillAI isn’t about a single guardian. It’s a swarm of adversarial AI agents, evolving through constant challenge probing users, contracts, and agents before attackers ever can. Check

Applications of GANs.- Image & video synthesis.- Style transfer & super-resolution.- Data augmentation for ML.- Creative industries (art, design, gaming).- Healthcare (drug & molecule generation). Learn more 👉

How Training Process works.1) Discriminator learns to tell real from fake. 2) Generator improves until its fakes can fool the discriminator. 3) Over time, generated data becomes indistinguishable from real. It’s an adversarial game → competition drives realism.

How GANs works ?.A GAN has 2 parts:.- Generator → creates fake samples from random noise. - Discriminator → checks if samples are real or fake. They train together in a feedback loop, pushing each other to improve.

Generative vs Discriminative.- Discriminative models → learn boundaries (dog 🐕 vs cat 🐈). - Generative models → learn the full data distribution & can create new samples (a “new cat” picture). GANs are one type of generative model.

Generative Adversarial Networks (GANs) are a major breakthrough in AI. They’re generative models, they don’t just classify data, they create new data that looks real. Example → GANs can generate photorealistic human faces that don’t exist.

RT @Wach_AI: 1/ Autonomous AI agents will eventually form their own economic networks, discovering each other, negotiating tasks, and excha….

The point is you can’t just “patch” AI security. You need defense-in-depth, red teaming, adversarial training & monitoring baked into every layer. Read in detail here 👇

MAESTRO breaks AI systems into 7 layers, from foundation models → data ops → agent frameworks → infra → observability → compliance → ecosystems. Each layer has its own threat landscape adversarial examples, supply chain hacks, poisoned data, manipulated metrics,.

Most security frameworks like STRIDE, PASTA weren’t made for AI agents. They miss adversarial ML, data poisoning, agent impersonation & the chaos of multi-agent ecosystems. That’s why we built MAESTRO, a framework tuned for the realities of Agentic AI.

RT @Wach_AI: The biggest attack surface for agent is it's chat interface. Our prompt verification uses an RL-guided adversary to jailbreak….

Based🟦, and swipin.

The real question:.Do we build AI agents that outpace attackers or wait until blackHats start training their own?. What do you think about the same ?. Because the next billion in exploits won’t be found by humans.

Why it changes the game:. • Scale → thousands of contracts stress-tested.• Adaptivity → evolves with new exploits.• Early warning → catch flaws pre-mainnet or mid-attack. This isn’t static auditing. It’s live fire drills for DeFi.

AI-red teaming = training adversarial AIs to act like hackers:.- Map attack paths across transaction graphs.- Detect wallet anomalies before they drain.- Generate nightmare test cases humans would miss.