Steganography

Steganography is the practice of hiding sensitive data, code, or malicious instructions within innocuous-looking files, images, or documents to evade detection by security systems and human reviewers. In AI governance contexts, steganography poses significant risks because it can be used to conceal unauthorized model modifications, inject hidden biases into training data, or embed prompt injections that bypass safety filters. Enterprise compliance teams must account for steganographic threats when auditing AI supply chains, validating model integrity, and protecting proprietary algorithms from covert manipulation.

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ResearchGlobal2026-09-19

Steganographic Attack Chain Turns Coding Agents Into Their Own Exploiters

Adversa AI's September 2026 security roundup documents a novel attack in which hidden content directs a coding agent to create an audit-hook wrapper and execute arbitrary remote code through it. The technique bypasses content-safety filters because the malicious instruction is embedded in a channel those filters do not inspect. Enterprises relying on text-prompt red-teaming alone are structurally exposed.

Adversa AI coding agents prompt injection red teaming steganography remote code execution agentic AI agent security