AI Security Risks and Emerging Tooling for Testing LLMs and Agentic Systems
Security reporting and vendor research highlighted accelerating AI/LLM security exposure as enterprises deploy generative AI and autonomous agents faster than defensive controls mature. Commonly cited weaknesses included prompt injection (reported as succeeding against a majority of tested LLMs), training-data poisoning, malicious packages in model repositories, and real-world deepfake-enabled fraud; one example referenced prior disclosure that a China-linked actor weaponized an autonomous coding/agent tool by breaking malicious objectives into benign-looking subtasks. Separately, commentary on AppSec programs argued that AI-assisted development is amplifying alert volumes and making traditional SAST triage increasingly impractical, pushing organizations toward more runtime and workflow-embedded testing approaches.
New and emerging tooling and practices are being positioned to address these risks, including an open-source scanner (Augustus, by Praetorian) that automates 210+ adversarial test techniques across 28 LLM providers as a portable Go binary intended for CI/CD and red-team workflows, and discussion of autonomous AI pentesting tools (e.g., Shannon) that require sensitive inputs such as source code, repo context, and API keys—raising governance and data-handling concerns even when used defensively. Several other items in the set (phishing/XWorm activity, healthcare extortion group “Insomnia,” Singapore telco intrusions attributed to UNC3886, and help-desk payroll fraud) describe unrelated threat activity and do not materially change the AI-security-focused picture.
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