Secure AI-Assisted Development: Five Critical Practices for Vibe Coding
Developing with AI has become the natural route to move an idea from concept to working code quickly. The problem is that most flaws in applications created this way do not originate from an error in the model but from an assumption made by the developer. The AI delivers exactly what was requested, and security is almost never part of that request.
Five points concentrate the majority of problems. Developers are advised to describe what the application must not do. Prompts usually detail functionality while ignoring restrictions. The AI implements the happy path with precision but does not imagine a malicious user on its own. When requesting a feature, teams should also specify who cannot access it, which values are invalid, and what must happen when someone attempts to bypass the flow. An undeclared restriction is a non-existent restriction.
Authentication and authorization are not the same. The AI implements login without difficulty, which is precisely where the trap lies. Authentication confirms who the user is; authorization defines what that user may access. Without explicit instruction, applications commonly verify only that someone is logged in and fail to check whether the record belongs to that user. Changing a number in the URL and viewing another client’s data remains the most frequently observed flaw in newly built applications.
Teams must review every dependency the AI selects. Each suggested library enters the project carrying its own history of vulnerabilities. Models tend to recommend packages that appear frequently in training data, which does not guarantee they are actively maintained or updated. Checking the last update date of each dependency and running an automated scan before release is recommended, because an inherited flaw is as exploitable as one written by the developer.
A secret removed from code does not disappear from the repository. An API key remains in commit history and stays accessible to anyone with repository access, as well as to automated scans that target public repositories. When a credential leaks, the only safe action is to revoke it and generate a new one rather than editing the file.
Business logic is the blind spot. No model knows the rules of a specific business. The AI does not understand that a coupon cannot be applied twice, that a balance should not accept a negative value, or that a cancelled order cannot generate repeated refunds. These flaws pass every automated scan because the code is technically correct. Only someone who understands the business flow can identify them.
Applications developed with AI have already entered the sights of cybercriminals, mainly because they repeat flaws that can be identified and exploited at scale. In addition to good practices during development, submitting the application to a pentest before production is advised. In this scenario, the HackerSec Pentest Platform has become an alternative used by developers and vibe coders seeking to test the cybersecurity of their applications with quality, agility, and a more accessible model. Rapid development is part of this new way of creating software.
Related articles
Developer Builds MCP Linter and Discovers Security Issues in Anthropic Official Servers
A developer has released mcp-linter, a lightweight security and compliance tool for the Model Context Protocol used by Anthropic's Claude ecosystem. The linter detects critical problems such as stdout pollution that breaks JSON-RPC framing, missing destructiveHint annotations that allow autonomous agents to execute dangerous actions without human confirmation, prompt injection vectors hidden inside tool descriptions, and incomplete input schemas that increase model hallucinations. When run against Anthropic's reference servers, the tool assigned low health scores to server-memory and especially server-filesystem, highlighting dozens of parameters lacking descriptions. The utility runs with zero dependencies, supports SARIF output for GitHub Security, and can generate automatic JSON patches. The project is open source under MIT license and already integrates into CI pipelines via a dedicated GitHub Action.
Pentesting AI Components: Key Vulnerabilities CISOs Must Address in RAG and AI Agents
The article examines how AI integration into corporate systems dramatically expands the attack surface beyond traditional web application testing. It details risks associated with chatbots, RAG-enabled systems that pull from internal documents, and autonomous AI agents capable of executing real actions via APIs. Yuri Grishaev, an OSCP+ certified specialist and top Standoff 365 hacker, explains how prompt injection can bypass access controls and lead to data leaks or unauthorized operations. The piece stresses that service accounts often hold broad privileges, allowing AI components to serve as indirect paths to sensitive data even when direct user access is restricted. It also covers how AI-generated output can trigger classic vulnerabilities such as XSS if not properly sanitized. Recommendations include inventorying all AI tools, enforcing least-privilege principles, and validating user permissions before any model processes or acts on retrieved content. ABP2B provides free checklists and policy templates to help organizations prepare for secure AI adoption.
Local LLMs Enable Scams by Drafting Fraudulent Replies Without Warnings
Tests on consumer hardware showed that popular 7-8B local models almost never flagged classic advance-fee schemes when asked to draft replies instead of detecting fraud. The author ran 42 scam scenarios across Russian, Kazakh and mixed language on Llama 3.1 8B, Qwen 2.5 7B and DeepSeek-R1 8B using only an RTX 5050 laptop and Ollama. Without additional controls the models produced ready-to-send messages agreeing to prepaid transfers to unknown Kaspi Gold numbers in nearly every case. Introduction of the MoneyGate layer, which combines a mandate check, a separate judge model and hard-coded red-flag rules, blocked all 42 malicious requests while still allowing most legitimate payments to proceed. The benchmark KZ Scam Shield covering 12 Kazakhstan-specific fraud patterns remains publicly available for further testing.
SOC Forum 2026: Experts Warn Generative AI Lowers Attacker Skill Barriers While Raising SOC Complexity
At SOC Forum 2026 in Moscow, cybersecurity leaders examined how generative AI reduces the expertise needed for cyberattacks while complicating defensive operations. BI.ZONE's Oleg Skulkin noted that AI has effectively lowered attacker preparation requirements to near zero. Kaspersky researcher Georgy Kucher warned that unverified AI-generated code can expose threat actors, and overly perfect Russian-language phishing text now triggers suspicion. Solar Group’s Alexander Shepilov advocated treating AI agents as distinct non-human identities with strictly limited privileges. Alexander Kuznetsov from the same company recommended gradual automation of SOC tasks rather than abrupt AI integration. Participants also cautioned that managing AI agents may increase spending on skilled personnel instead of delivering cost savings.