An AI-native Approach to Discovering Logic & Authorization Flaws

If you’re struggling with Authorization Mismatch, Authorization Bypass (e.g. IDOR, BOLA), authorization flaws, or you just can’t effectively identify complex logic flaws in modern API code, then this whitepaper is for you. It covers an LLM-native approach that combines deterministic evaluation and contextual reasoning to solve this problem at speed and at scale.

“LLMs have unlocked lots of opportunities to innovate in the vulnerability management space. For something like Authorization and IDOR issues, that have traditionally been very difficult to discover and remediate at scale, we are finally at a frontier where we can use a combination of deterministic (rule based) and probabilistic (LLMs).”
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—Anshuman Bhartiya, The Boring AppSec Podcast, @Lyft

In this paper, you’ll see a real-world Go API use case, and you’ll discover how drawing on large language models (LLMs) incorporates a deeper level of semantic understanding and reasoning, makes security solutions better equipped to handle logic-based flaws, and offers more accurate authorization detection.Plus, you’ll see how DryRun Security provides:

Adaptive Analysis: We leverage deep knowledge of the customer’s codebase to locate codepaths that may bypass authorization checks—even if they’re custom or generated.

Augmented Human Review: DryRun Security flags suspicious paths for manual validation, assisting security teams in focusing their efforts on the most critical vulnerabilities.

Scalability: Large codebases can quickly overwhelm human reviewers and traditional SAST rules, yet our LLM-based method excels in precisely these environments—uncovering logic flaws that other tools miss.