Defeating "Bandaid Solutions" (blog.zast.ai)

A client case study showed ZAST.AI successfully detecting and validating a command injection vulnerability even after two obfuscation “patches.” In the initial assessment ZAST.AI flagged a taint source and taint sink and produced a working POC that executed a shell command. The client then applied a superficial fix—Base64-encoding inputs—which ZAST.AI decoded and proved still allowed execution. A second attempted mitigation added a prefix check ("secret") and stripped the prefix via .substring(6), but ZAST.AI combined Base64 decoding, prefix matching, and crafted HTTP headers to generate a POC that bypassed the checks and demonstrated remote code execution. Technically, ZAST.AI links taint sinks to their sources and uses a large language model to synthesize, mutate, and verify exploit payloads dynamically, not just rely on predefined test cases. That hybrid taint-analysis + LLM approach exposes weaknesses that both black-box (limited scenarios) and white-box (knowledge-heavy) methods can miss, reducing the risk of false security confidence from “band‑aid” fixes. The case underscores the value of LLM-driven dynamic verification for real-world vulnerability triage and remediation planning; ZAST.AI also notes upcoming Python support and IDE extensions to broaden its coverage.