AI assistants boost speed, but they can also introduce insecure code and overconfident errors. A research study, Do Users Write More Insecure Code with AI Assistants? found that users with an AI assistant produced less secure code and felt more confident in the results. AI coding assistants like Copilot and CodeWhisperer are changing how teams write code. They help explain unfamiliar application programming interfaces (APIs) and highlight where security risks are likely to appear.
One thing is certain, AI coding assistant security deserves its own plan. The risk is not abstract. For instance, in a controlled user study, developers with an AI assistant produced less secure solutions than those without the tool. Many also believed their code was safe when, in fact, it wasn’t. Speed, coupled with unjustified confidence, amplifies risk.
Prompt injection and insecure output handling are top risks for large language model (LLM) apps. These issues can lead to data exfiltration and unsafe actions if you don’t filter or constrain outputs. Secret exposure is surging. GitHub reports there were 39 million leaked secrets in 2024 across public repositories, highlighting the importance of secret scanning and guardrails. Dependency risk remains high. Furthermore, supply chain risk compounds these issues. AI suggestions may include outdated or risky packages, and attackers continue to seed malicious and vulnerable components across registries.
Security leaders shouldn’t ban assistants. Rather, they should govern them. The U.S. Cybersecurity & Infrastructure Security Agency’s 2024 joint guidance sets practical deployment controls for externally developed AI systems. The UK National Cyber Security Centre (NCSC) Guidelines for secure AI system development aligns with “secure by default” practices. Map these to your software development lifecycle (SDLC) and your LLM use cases. Add controls for prompts, outputs, data paths, and tool access. By embedding these measures, organizations can gain the benefits of AI without sacrificing resilience.
AI-assisted coding accelerates development but also introduces new risks that require strong guardrails and AI assistant governance for developers.
AI assistants can suggest libraries that look popular but lag on patches. They can also auto-add imports that pull in vulnerable transitive dependencies. This creates hidden security debt across your repos. Recent research quantifies the problem. A 2025 paper, A dataset on vulnerabilities affecting dependencies in software package managers, shows 6.93% of npm versions and 7.5% of RubyGems versions rely on at least one vulnerable dependency. PyPI has lower percentages, but still shows the affected latest versions. This confirms that just trusting the import is unsafe. You need automated checks around suggestions and updates.
Assistants can generate functions that pass trivial tests but fail under edge cases. The pattern appears in controlled studies. Users often accept plausible but unsafe code. They also grow more confident in that code when an assistant produces it. That can hide input-validation gaps, insecure defaults, or misuse of crypto. The Common Weakeness Enumeration (CWE) Top 25 Most Dangerous Software Weaknesses shows how common and dangerous these weakness classes are. Model-generated code that skips validation, sanitization, or error handling can map directly to these patterns.
You should assume that first drafts from AI are untrusted. However, they will look tidy and idiomatic, which makes reviews harder, not easier. Build guardrails to keep insecure snippets from landing in production. Add tests that assert safe behavior, not just happy paths. Use property-based testing to generate thousands of adversarial inputs per property. Presented at the 2024 International Conference on Software Engineering (ICSE), the report Property-Based Testing in Practice, shows both growing industrial use and substantial bug-finding benefits. Target standard classes. Include unchecked input sanitization, broken crypto defaults, insecure randomness, improper error handling, and authorization bypasses.
Secrets appear in prompts during debugging and support conversations. They appear in code comments that models read. They also appear in continuous integration (CI) logs that AI assistants summarize. The scale of secret exposure is now tracked at the platform level. Treat any assistant channel as a potential data exfiltration path. Adopt defensive defaults. Mask and tokenize in development tools. Strip secrets from stack traces and error payloads. Enforce secret scanning in pre-commit and in CI. Train developers to use synthetic or redacted values in prompts. Then verify with automated checks that run continuously. Guidance from CISA and NCSC provides concrete logging, monitoring, and incident workflows for AI systems.
Copying proprietary code into an external assistant can expose trade secrets. This risk is tangible and measurable. An automated attack (PLeak) exactly reconstructed system prompts for 68% of 50 real-world apps on Poe. 55% of apps there keep prompts confidential, showing operators view prompts as intellectual property (IP).
Training data extraction is possible. A USENIX security study, Extracting Training Data from Large Language Models, generated 1,800 candidates and confirmed 600+ verbatim training samples. The best attack configuration yielded 67% verbatim training examples. Extracted data included code and unique identifiers. This shows leakage persists even with defenses.
A 2024 study, Prompt Leakage effect and defense strategies for multi-turn LLM interactions, reported an average attack success rate of 5.3% against black-box models after implementing layered mitigations. Sensitive instructions and attached knowledge still leaked. Code generation can copy licensed material. A 2025 study, LiCoEval: Evaluating LLMs on License Compliance in Code Generation, found 0.88%-2.01% of model outputs were strikingly similar to existing open-source code, often without correct license attribution. This creates legal exposure.
The code ecosystem is duplication-heavy. A prior large-scale analysis of data leakage in LLMs across 4.5 million GitHub projects found that approximately 70% of code files are clones. High duplication raises the chance of near-verbatim reproduction and accidental IP conflicts. It’s crucial to mitigate with scope and controls. Limit the assistant to specific repositories, branches, and paths. Log every file the assistant reads and writes. Keep immutable audit trails for legal and incident response. Validate outbound responses and block exports that include regulated or proprietary text.
Securing AI-assisted development requires disciplined practices to prevent leaks and unsafe code from reaching production.
Never include credentials, customer data, or proprietary algorithms in prompts. Use synthetic data or redacted examples. Treat every prompt as a record that may be logged. This aligns with NCSC guidance on secure operation and the safe deployment of AI coding tools. Build prompt templates that avoid sensitive fields by default. Add server-side filters that remove secrets before a request leaves your network.
Tie prompting to policy. Use attribute-based rules to block certain data classes from reaching external models. Record the purpose and legal basis for the data used in prompts. Keep retention short and auditable. Link these practices with your AI governance best practices.
Treat model output like code from an intern. Review it, lint it, and test it. Add static application security testing (SAST) and dynamic application security testing (DAST) runs before the merge. Insist on unit and property tests that cover failure and adversarial inputs. Ensure checks align with the CWE Top 25, allowing reviewers to focus on high-impact patterns. Ask reviewers to state whether the change contains AI-generated code and what extra checks were performed. Peer review is necessary but not sufficient. Make CI your gate. Block merges on failed security checks. Keep evidence in your pipeline logs. That creates accountability without slowing teams when code is clean.
Scope assistant access to the minimum needed repos and paths. Do not grant organization-wide read access unless absolutely necessary. Use short-lived tokens and rotate them. Disallow pushes or PR creation from assistant identities in production repos.
Both NCSC and CISA guidance emphasize the importance of strong identity and access control for AI components. Apply the same standards you apply to human developers, then narrow further. Next, record and review assistant actions. Treat them as a service account subject to continuous monitoring. Add attribute-based access control (ABAC) or policy-based access control (PBAC) policies that down-scope access based on project, data class, or environment.
Run AI-generated code in a sandbox first and use throwaway environments. Keep permissions minimal. Block outbound traffic by default and allow only what tests need. Snapshot the environment so you can reset fast between runs. Add canary tests that detect risky behaviors. Look for unexpected system calls, shell outs, file reads, and outbound requests.
Unpatched vulnerabilities remain an important security threat to today’s businesses. The paper, Phoenix: Surviving Unpatched Vulnerabilities via Accurate and Efficient Filtering of Syscall Sequences proposes a runtime protection system that filters malicious syscall sequences with low overhead. In application-level tests, Phoenix kept response-time overhead below 3% per call and average latency increases around 4%, while achieving 100% detection accuracy with no false positives under normal load. This allows you to react fast when a suggestion attempts to exfiltrate data or spawn processes. Keep alerts simple and explain why a test failed to shorten rework loops.
As AI becomes part of daily coding, developers need structured tools and workflows to keep pace without sacrificing security. Adopting proven safeguards ensures that assistant-generated code is reviewed, tested, and contained before it is deployed in production.
Embed SAST, DAST, and dependency scanning into CI on every pull request. Enforce dependency policies and continuous integration and continuous delivery (CI/CD) checks to block packages with known Common Vulnerabilities and Exposures (CVEs), preventing risky libraries from entering your repositories.
An Empirical Study of Static Analysis Tools for Secure Code Review published in 2024, studied 92 C and C++ projects, and found SAST produced flags in vulnerable functions for 52% of vulnerability-contributing commits. Prioritizing changed functions improved precision by 12% and recall by 5.6%. This supports “fail fast” gates in CI. However, adoption is still low. Only 20% of 1,003 surveyed developers reported using SAST tools, according to another 2024 large-scale empirical study.
Local, easy-to-run checks can close this gap. Use review rules for AI-authored changes, as automated, defect-focused review is linked to fewer severe incidents. According to a 2025 report, Towards Practical Defect-Focused Automated Code Review, in 2024, 67% of business-critical failures were change-related and 19.54% of graded incidents were tied to code changes. Make reviewers acknowledge security risks when AI writes code.
Use a platform to enforce policy around coding assistants. Kirin secures AI-powered development by adding guardrails that prevent unsafe code suggestions, secret leakage, and misuse of connected tools. It validates outputs across contexts to ensure consistent enforcement, blocks risky behaviors before they reach production, and monitors policy drift over time so safeguards remain effective without slowing developers down.
Attributes naturally change as roles, teams, projec ts, and data classifications evolve, creating silent over-permission if left unchecked. KIRIN snapshots attributes at decision time and compares them to policy baselines, checking factors such as repo visibility, branch protections, data-class tags, device posture, and session risk. When drift is detected, it can block the action, trigger step-up authentication, or route the request for review. Because most incidents follow change, catching drift early prevents regressions and risky access expansion. Every drift event is logged, allowing reviewers to see exactly what changed and why, thereby strengthening both security and auditability.
Why does this matter? According to the automated code review paper mentioned above, in 2024, change-related failures drove 67% of core system incidents. Guardrails and automated review reduce that risk by catching issues before they are merged. Supply-chain risk is rising in AI stacks. A 2025 empirical study of 52 open-source LLM projects mapped vulnerable third-party dependencies and slow remediation patterns. Platforms that combine prompt controls with dependency and secret monitoring close these gaps. For program design and measurement, see Knostic’s AI Security Audit.
Good guardrails increase velocity by removing busywork and reducing rework. Empirical data show that changed-function-focused SAST improves reviewer signal. Precision rose by 12% and recall by 5.6% in evaluation, according to the empirical study of static analysis tools mentioned earlier. That means fewer noisy alerts and faster reviews.
Keep checks close to the integrated development environment (IDE) and pull request (PR). Only 20% of developers use SAST today, so friction must be low. Local pre-commit and one-click CI runs help adoption, while reviews for high-risk areas should be automated.
In 2024, change-related failures dominated incidents. Tie automation to these hotspots to save time and reduce severity, and use continuous evidence. In Large Language Models and Code Security, security research concluded that robust testing and scanning are needed alongside assistants. You should also keep artifacts and logs so fixes are fast and auditable.
Want to see how Kirin keeps your AI coding assistants secure and your codebase clean? Explore Kirin’s AI Security Platform for more information.
A coding assistant is an AI tool that suggests or edits code, explains APIs, writes tests, or automates routine tasks. It works inside IDEs or through chat, but its output should be treated as untrusted until validated. Organizations like OWASP and CISA provide guidance on safe use.
AI-generated code can be correct yet still unsafe. A controlled study found that developers using an assistant wrote less secure code while feeling more confident in it. To counter this, your process should include tests and reviews tuned to standard weakness classes.
Limit repo and data access, filter prompts and outputs, and scan dependencies and code in CI. Add sandboxing and least-privilege, and follow NCSC and CISA deployment guidance to operationalize these steps.