Security scans, model evaluations, and static analyzers can each pass an AI application individually while the system remains exploitable end to end, because chained risk emerges across the seams between prompt templates, RAG, tool calls, and MCP endpoints rather than inside a single component. A framework of three testing lenses is proposed: DAST to map exposed surface, AI penetration testing to prove exploitability with statistical confidence, and AI red teaming to demonstrate business-impact objectives like data exfiltration. The piece argues these lenses must be orchestrated into a unified testing harness rather than run as disconnected vendor engagements, and promotes a companion whitepaper covering the economics and architecture of such a program.
Table of contents
Chained risk breaks the isolated-vulnerability modelThe three lenses of adversarial AI testing and why just one isn’t enoughThe solution: orchestration over isolationWant the full operational and economic blueprint?Chained Risk: The Operating Model and Economics of Adversarial TestingQuestions this post answers
why would an AI application still be exploitable even if DAST scans and jailbreak evaluations both pass
Because each security tool only sees its own narrow layer, while the actual vulnerability emerges from chaining behavior across layers. A DAST scan can report zero open endpoints and a model evaluation can report a passing jailbreak safety score, yet an attacker can still steer the LLM to invoke an internal tool that bridges an untrusted prompt to a backend execution sink, since no single scan traces that cross-layer path. Teams hardening AI stacks against chained exploits can follow ongoing AI security coverage on daily.dev.
what is the difference between AI penetration testing and AI red teaming
AI penetration testing validates whether a single component or tool call can be exploited, running repeated trials to establish a statistical success rate (for example, proving a guardrail bypass succeeds 30% of the time) but it does not trace how that exploit propagates through a full business process. AI red teaming instead sets an adversarial objective, such as exfiltrating a customer database, and chains primitives across application, model, tool, and data layers to reach it, though it is slower and more resource-intensive. Developers choosing between AI testing approaches can track comparisons like this on daily.dev.