Claude Code Security is a research preview tool developed by Anthropic, leveraging the Claude large language model to identify and remediate software vulnerabilities. It aims to provide a seamless scan-to-fix workflow for enterprise and team customers, functioning as a sophisticated security researcher that understands code context, traces data flows, and proposes actionable fixes.

The core methodology of Claude Code Security departs from traditional, rule-based pattern-matching tools by employing a deep reasoning approach. It utilizes its underlying large language models, described as Anthropic's "most capable models" and the same ones used to secure Anthropic's own codebase, to:

1. Semantic Understanding and Contextual Analysis: Unlike static analysis tools that often rely on regular expressions or predefined vulnerability signatures, Claude Code Security semantically interprets code. It "reasons through your code like a skilled security researcher," implying an ability to build an internal representation of the code's logic, control flow, and data flow across multiple files. This allows it to understand complex interactions and dependencies that contribute to vulnerabilities, even those that span across different components or modules. The model's capacity to "understand context" extends to discerning the purpose and intent behind code segments, enabling it to identify flaws that depend on specific environmental or operational conditions.
2. Data Flow Tracing and Multi-Component Vulnerability Detection: The system is capable of "tracing data flows across files." This means it can follow the lifecycle of data, from input points to potential sinks, identifying how data transformations and transfers might lead to security weaknesses. This capability is critical for detecting complex, multi-component vulnerability patterns, such as injection flaws (e.g., SQL injection, command injection) where unsanitized user input propagates through several layers of code before being executed in a sensitive context. It can also read Git history to augment its understanding, suggesting a capability to analyze code evolution and potential regression vulnerabilities.
3. Adversarial Verification for False Positive Reduction: A distinctive feature is its "adversarial verification pass." Before surfacing a finding to the user, Claude "challenges its own results." This internal validation mechanism suggests an iterative process where the model attempts to disprove or confirm its initial vulnerability detection. This self-correction or "adversarial" process aims to significantly reduce false positives, which are a common pain point with traditional security scanners, thereby improving the signal-to-noise ratio for security analysts.
4. Automated Patch Generation: Upon identifying a vulnerability, Claude Code Security not only reports the issue but also generates a "recommended patch." These suggested fixes are designed to be "targeted" and "maintain your code's structure and style," facilitating quick remediation. This closes the loop on vulnerability management, moving beyond mere detection to actionable resolution. The process, however, maintains "full remediation control," requiring human review and approval for every proposed patch, acknowledging that the AI can make mistakes.

Claude Code Security primarily targets "high-severity vulnerabilities," including memory corruption, injection flaws, authentication bypasses, and complex logic errors that are often missed by pattern-matching tools. It complements existing security tools by detecting issues they might miss and streamlining the remediation process. It is currently available as a limited research preview for Claude Enterprise and Claude Team customers through Claude Code on the Web.