Why Relying Only on Claude for Code Security Review Fails Growing Teams

Source: Dev.to

Introduction

The first time you see an AI comment on a pull request, the feedback loop stands out. A full review appears in seconds, pointing out potential issues before a human reviewer has even opened the file. The appeal of using a tool like Claude for code‑security review—a critical part of security in the SDLC—is clear: catch problems early and reduce the team’s manual workload.

In practice, however, this speed often creates a false sense of security. It works well at first, but starts to break down as the team grows and systems become more complex.

The Critical Blind Spot

A good security review depends on context that is not in the diff. That context lives outside the isolated code. By nature, an LLM does not have access to it. It analyzes only a slice of the code, in isolation, and misses the broader view where, in practice, the most serious vulnerabilities usually live.

Architectural and Data‑Flow Risks That Go Unnoticed

• Many critical security flaws are not in the code itself, but in how data flows between components.
• An LLM does not know the system’s trust boundaries. For example, it may not know that UserService is internal‑only, or that any data coming from a publicly exposed APIGateway must be re‑validated, regardless of prior validations.

Example: Cross‑Tenant Authorization Flaw

1. A developer adds a new endpoint that correctly checks whether the user has the admin role.
2. Looking only at the diff, the code looks correct.
3. A senior engineer knows an implicit system rule: an admin from Tenant A should never access data from Tenant B. The code does not check the tenant ID.

Claude will not flag this because it does not understand your multi‑tenancy model or the internal rules around data isolation and sensitivity. It sees a valid role check and moves on, letting a potential cross‑tenant data leak slip through.

Ignoring Repository History and the Evolution of Threats

• A codebase is a living document. The history of commits, pull requests, and incident reports contains valuable security context.
• A human reviewer may remember a past incident involving incomplete input validation on a specific data model and will be extra alert to similar changes. An LLM has no memory of this.

Example: Re‑introducing a Known DoS Vulnerability

# Six months ago
- Fixed a denial‑of‑service issue by adding a hard size limit to a free‑text field.

# New change
+ Added a similar free‑text field but omitted the size validation.

The code is syntactically correct, but it re‑introduces a known vulnerability pattern. An experienced reviewer spots this immediately; an LLM sees only the new code, with no access to lessons learned in the past.

Inability to Learn Team‑Specific Security Policies

Every engineering team develops its own set of security conventions and policies. They are often domain‑specific and not always explicit in the code.

An LLM can inadvertently suggest a solution that directly violates these rules, creating extra work for the reviewer, who now has to fix both the code and the AI’s suggestion. The confident, authoritative tone of an LLM can lead junior developers to assume its suggestions represent best practices, even when they contradict established standards.

Scaling Traps: When LLM Limitations Add Up

For a small team working on a monolith, some of these gaps may be manageable. But as the organization tries to scale code review across a growing team, more engineers, more services, and more micro‑services, these limitations create systemic problems that automation alone cannot solve.

The Human Verification Bottleneck

• Reviewing the AI’s own output becomes a new chore.
• With a constant stream of low‑impact or irrelevant suggestions, engineers quickly develop alert fatigue and start treating AI comments like linter noise—something easy to ignore.

In practice, every AI‑generated comment still requires someone to assess its validity, impact, and context. This slows reviews down and pulls attention away from what actually matters. The cognitive load of filtering AI noise can easily outweigh the benefit of catching a few obvious issues.

Architectural Understanding Gaps in LLM‑Based Code Security Reviews

• In distributed systems, the most dangerous bugs usually live in the interactions between services.
• An LLM reviewing a change in a single repository has no visibility into how that change might break an implicit contract with a downstream consumer.

Example: Breaking a JSON Contract

- "userId": "12345",
- "email": "alice@example.com",
+ "userId": "12345"

Removing the email field may cause silent failures in another team’s service that depends on it—something an LLM cannot detect.

• The same applies to cryptography errors. An LLM can flag obvious problems (e.g., use of DES) but tends to miss harder‑to‑detect flaws, such as reusing an initialization vector (IV) in a block cipher. Identifying this requires understanding application state and data flow across multiple requests, far beyond static analysis of a snippet.

Hallucinations

LLMs can be wrong with a lot of confidence. It is not uncommon to see:

• Recommendations for security libraries that do not exist.
• Incorrect interpretations of details from a real CVE.
• Broken code snippets presented as a “fix.”

In security, this is especially dangerous. A developer may accept an explanation that sounds plausible but is wrong, and end up introducing new vulnerabilities.

Takeaways

1. AI can accelerate low‑level syntactic checks, but it cannot replace the contextual, historical, and architectural insight that human reviewers bring.
2. Treat AI suggestions as advice, not authority—always verify against your team’s policies, system design, and threat history.
3. Invest in tooling that augments human reviewers (e.g., data‑flow analysis, policy‑as‑code, provenance tracking) rather than relying solely on LLMs.
4. Establish clear guardrails:
   • Explicitly encode team security policies in a machine‑readable format.
   • Integrate LLM output with existing static analysis and runtime security tools.
   • Provide feedback loops so the model’s suggestions improve over time without hallucinating.

By recognizing the critical blind spot of LLM‑based code‑security reviews and designing processes that combine AI speed with human depth, teams can reap the benefits of automation while maintaining a robust security posture.

Introducing New Vulnerabilities While Fixing Others

Creating a new vulnerability while trying to fix another one gives a false sense of confidence. This undermines learning and can lead to a worse security outcome than the original issue.

Why Human Expertise Still Matters

• AI tools are valuable, but they should augment human judgment, not replace it.
• Human reviewers bring essential context that machines cannot provide.

Beyond Syntax: Business Logic and Intent

A senior engineer understands the why behind the code. They can connect a proposed change to its business goal and ask critical questions that an LLM would never consider, such as:

“What happens if a user uploads a file with more than 255 characters in the name?”
“Is this new user permission aligned with the company’s GDPR compliance requirements?”

This kind of reasoning about real‑world impact is the foundation of a good security review.

Mentorship and Building a Security Culture

• Code reviews are a primary mechanism for knowledge transfer within a team.
• When a senior engineer points out a security flaw, they don’t just say “this is wrong.” They:
  1. Explain the risk.
  2. Reference a past decision or internal document.
  3. Use the review as a learning moment.

The result is heightened security awareness across the entire team and a culture of shared responsibility. An automated bot comment offers none of that—it feels like just another checklist item to clear.

A Hybrid Review Model

The goal isn’t to reject new tools but to be intentional about how they’re used. A healthy security posture uses automation to augment human judgment, not replace it.

Augment, Not Replace: Where LLMs Make Sense

The best use of LLMs in code review is as a first automated pass for a very specific class of problems. For example:

• Hard‑coded secrets and API keys
• Use of known insecure libraries or functions (e.g., strcpy in C, pickle in Python)
• Common patterns indicating SQL injection or XSS

The LLM’s output should be treated as a suggestion, not a verdict. Final authority still rests with the human reviewer.

Invest in Context

Getting consistently useful results from an LLM requires significant investment in providing the right context, such as:

• Architectural diagrams
• Data‑flow information
• Internal team policies

These inputs often need advanced prompt‑engineering practices and must be kept up‑to‑date, creating an ongoing maintenance burden. Before making an LLM a mandatory step in CI/CD, it’s essential to understand that cost and its limits.

Cultivate a Strong Security Posture to Scale

In the end, a strong security culture depends on human judgment. Automation works well for simple, repetitive, and context‑free tasks, freeing experienced engineers to focus on complex, dependency‑heavy risks where experience truly matters.

Balancing the efficiency of automation with the judgment of those who know the system is the only way to build a security practice that truly scales.