The AI trap when encoding opens security gaps

In recent years, many development teams have incorporated artificial intelligence models into their workflow to "accelerate" code writing: what some call colloquially "vibe coding." These tools can save hours and unlock creative solutions, but also bring a silent risk when the suggested code is accepted without well understanding your internal decisions. A recent example documented by the security signature Intruder It clearly illustrates how a poorly placed line of trust can transform aid into an open door to manipulation.

Intruder describes how, by building an honeypot for its quick response service, they used an IA model to outline a component that would record the visitors' activity. The goal was deliberately insecure - it was infrastructure designed to attract and observe attacks - and the team reviewed the code before it was launched. However, a few weeks later, the logs began to show names of strange files: instead of being tagged by original IP addresses, chains appeared that were clearly part of the payloads sent by attackers.

The internal investigation revealed the cause: the fragment generated by the IA was reading headers that the client can control (e.g., X-Forwarded-For) and treated them as the visitor's IP address. This assumption is only safe when the headers are actually injected by a proxy of trust; in environments exposed to the public, those headers are completely under user control and can be manipulated to inject data or supplant identities. In this particular case the impact was left in manipulation of directory names and absence of complete attack chain, but the same failure could have escalated to more serious problems such as local file disclosure or Server-Side Request Forgery (SSRF), well-documented vulnerability classes by projects such as OWASP and related to improper access to the file system ( path traversal / LFI).

An important point highlighted by the team was that popular static analysis tools did not detect the problem. They ran scanners like Semgrep OSS and Gosec and although they reported minor improvements, they did not mark the unsafe dependence on external headers. This is not a failure of the tools itself, but a limitation of the approach: many vulnerabilities require context, intent and understanding of the limits of trust between components - nuances that the purely syntactic and local analysis usually overlook.

The experience of the Intruder team also illustrates a human phenomenon known in highly automated domains: monitoring a task performed by a machine can involve less mental commitment than performing the task yourself, and that can lead to more relaxed supervision. In aviation and other fields, it has been studied how automation generates bias of confidence and complacency; in software development something similar happens: the code "not written by us" is not always internalized with the same depth and this reduces the quality of the review.

The problems do not end with a single example. Intruder shares that other interactions with reasoning models led to unsafe IAM roles configurations in AWS, until after several human iterations and corrections a safe configuration was achieved. This experience is consistent with recent research findings that have identified hundreds or thousands of vulnerabilities in applications created with the help of coding- as- a- service platforms: see the methodology and results of Scape.tech provides a solid perspective on the scale of the problem.

It is not that models "lie" deliberately; rather, they produce results that often seem plausible and well-formed, which makes it easier for uncautious reviewers to accept solutions without contrasting critical assumptions. Therefore, the responsibility lies with the organizations that integrate these tools within their development chain: end-users have no way of distinguishing whether the software they use contains fragments generated by IA and therefore depend on companies to apply additional controls.

What practical lessons to draw? First, the IA should be understood and managed as an assistant who suggests, not an authorized author. Teams should ensure that only profiles with technical training and safety sensitivity use these tools to produce critical code. Second, code reviews and continuous integration pipelines need to evolve: incorporate dynamic tests that validate confidence limits, malicious entry scenarios and production behaviors, as well as classic static analysis. Third, it is crucial to apply specific defenses in the code: never rely on headers provided by the unvalidated customer or a reliable proxy that normalizes them; heal and normalize names used to create routes; and prefer APIs or framework utilities that expose the real remote direction when there is a controlled proxy chain.

In addition to development practices, it is appropriate to rely on consolidated frameworks and guides. Organizations such as OWASP offer resources on web risks and patterns to mitigate them, while the NIST promotes frameworks for integrating security into the software life cycle. Adopting these standards and adapting them to the new IA-assisted coding context helps prevent subtle failures from hanging in production.

The conclusion is not to give up the developing IA: the benefits in productivity and creativity are real. But the story of Intruder works as a preventive alert: when the machine suggests, the human must verify rigorously. With best practices, reinforced reviews and context-adapted detections, it is possible to take advantage of the speed of the IA without giving by default the safety to luck. For those who want to deepen, the above-mentioned reports and tools - the Intruder post, the Scape.tech methodology, and repositories like Semgrep- are good starting points to understand to what extent this new way of encoding is going to change the risk surface in the software.