𝗔𝗜 𝗜𝘀 𝗠𝗮𝗸𝗶𝗻𝗴 𝗨𝘀 𝗠𝗼𝗿𝗲 𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗹𝗲
Security incidents feel different now.
Breaches and phishing attacks are not new. But they are growing in scale and speed. Instagram accounts get hacked overnight. Corporate systems fail in hours. Phishing emails now sound human.
AI is causing this in two ways.
First, we are adding AI to our systems without enough security. Every new AI feature adds new risks.
AI systems need:
- Large data pipelines
- APIs to connect services
- Third-party models and tools
- Real-time processing
Companies add AI faster than security teams can audit them. In 2026, hackers used AI-powered support systems to hack Instagram. They did not target code. They targeted the AI recovery flow.
Second, attackers use AI to strike.
Phishing used to be easy to spot. Now, AI writes perfect emails. It scrapes your LinkedIn to sound like your manager. One platform, Tycoon2FA, sent millions of emails and bypassed MFA tokens.
Attackers also use AI for:
- Prompt injection to bypass rules
- Model poisoning to corrupt data
- API abuse to steal information
- Voice cloning and deepfakes
The TanStack incident shows the danger of supply chain attacks. An attacker used GitHub Actions to poison the CI/CD cache. This hit popular packages used by millions. It even impacted companies like OpenAI and Vercel. Every npm install is a trust decision.
How to protect yourself:
- Audit your dependencies regularly
- Harden your CI/CD pipelines
- Limit permissions for AI integrations
- Watch for prompt injection risks
- Train your team to spot AI social engineering
AI is not the enemy. The danger comes from moving too fast without security.
As a developer, how do you handle security in your projects? Did the TanStack incident change how you use dependencies?
Share your thoughts below.
Optional learning community: https://t.me/GyaanSetuAi