How Copilot amplifies insecure codebases
Copilot writes ~46% of code where enabled, and studies show ~40% of its security-relevant suggestions are vulnerable. Here's the data on the risk.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Copilot writes ~46% of code where enabled, and studies show ~40% of its security-relevant suggestions are vulnerable. Here's the data on the risk.
AI now writes up to 40%+ of new code, and models hallucinate nonexistent packages in 5-22% of outputs. Here's why Black Duck-style SCA misses that risk, and what controls actually work.
GitHub Copilot has 1.3M+ paid seats. Five concrete, evidence-based practices for locking down content exclusion, licensing, code quality, and prompt injection risk.
LLMs hallucinate nonexistent packages in up to 1 in 5 code samples — and slopsquatting attacks are already exploiting that predictability in the wild.
We field-tested five GenAI code review tools against 240 seeded security defects to see which catch real issues and which hallucinate findings.
AI and ML pipelines introduce unique supply chain risks -- from poisoned training data to compromised model registries. Here is what attackers are targeting and how to defend.
Running an open-weight model inside an enterprise perimeter seems safer than calling a hosted API. It is, and it isn't. The sandboxing patterns that actually produce the safety properties.
Security in AI systems isn't a wholly new discipline, but prompt injection, training data provenance, and model supply chains introduce risks traditional AppSec tooling wasn't built to catch.
As AI agents now author up to half of production commits, Safeguard introduces Agentic Development Security (ADS) — a new framework for securing autonomous coding.
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