Generative AI (GenAI) is reshaping how software is built. Tools like GitHub, Copilot, ChatGPT and Replit Ghostwriter have rapidly become indispensable in the modern development toolkit, promising increased throughput, reduced toil and faster time-to-market. They suggest code snippets, automate documentation, predict bugs, and even guide architectural decisions. We’re entering an era where developers don’t just write code; they collaborate with machines that do it for them.
But this speed comes at a cost: A rising wave of exploitable vulnerabilities baked into AI-generated code.
A paradox is emerging. The same tools enabling rapid innovation also reintroduce legacy vulnerabilities, spread insecure patterns, and inadvertently create fertile ground for attackers. As development cycles shorten, attackers are moving faster, too, using AI to scan for flaws and weaponize zero-days in record time.
This is not a call to abandon GenAI. Instead, it’s a call to reset expectations, policies and developer training to ensure that GenAI remains a co-pilot, not an autopilot, on the road to secure software.
The Rise of AI-Accelerated Development
GenAI has fundamentally altered the software development lifecycle. From automating repetitive tasks to suggesting code and generating documentation, these solutions can assist developers in their attempts to work faster and focus on high-value problem-solving. Organizations adopting GenAI-powered workflows often see productivity boosts and reduced burnout, particularly for junior developers who benefit from real-time guidance.
GenAI is becoming as essential as version control or CI/CD pipelines for many. But this normalization is where the risk begins to grow.
Blind Spots in the Code
AI-generated code often lacks context, particularly around security. These tools train on massive public datasets that include flawed, outdated, or insecure examples. As a result, GenAI may reproduce known bad practices without warning or the developer noticing.
Some of the most common vulnerabilities now appearing in AI-assisted code include:
- Cross-Site Scripting (XSS)
- Cross-Site Request Forgery (CSRF)
- Insecure deserialization
- Hardcoded credentials
- Open redirects
In some cases, GenAI tools have even reproduced critical, previously patched vulnerabilities. For example, code resembling the famous Log4Shell vulnerability (CVE-2021-44228) has surfaced in AI-generated outputs. While rare or accidental, their reappearance suggests a bigger issue in which GenAI lacks the “judgment” to avoid what it doesn’t understand.
And this is just the beginning. As attackers also leverage GenAI to chain vulnerabilities or create polymorphic malware, the time between a zero-day’s discovery and its exploitation continues to shrink.
The Illusion of Trust
A particularly dangerous assumption among developers is that AI-suggested code is “safe” because it’s syntactically correct or comes from a trusted tool. But code that compiles isn’t necessarily secure.
Because GenAI outputs often appear polished, developers may skip critical steps, code reviews, security audits, or proper documentation checks. This false sense of security is especially risky for junior developers or fast-moving teams under delivery pressure.
Even worse are AI hallucinations, known as syntactically valid outputs but semantically nonsensical. When this happens in a chatbot, it’s amusing. In production software, it’s a backdoor waiting to be exploited.
Best Practices for a GenAI World
Security teams and developer leads should resist the urge to treat GenAI as a plug-and-play solution. Instead, organizations must design policies and workflows that integrate GenAI safely and responsibly.
Recommended practices include:
- Always verify GenAI-generated code with linters, static analyzers, and security scanning tools.
- Cross-reference suggestions with official documentation or vetted code libraries.
- Avoid copy-pasting GenAI output into production environments without manual review.
- Incorporate secure coding training that explicitly addresses GenAI workflows.
- Include GenAI in your DevSecOps pipeline, with checkpoints for security and compliance.
Regulations are Catching Up
Recent policy changes highlight the urgency of responsible AI usage. The EU AI Act and U.S. Executive Order 14110 emphasizes human oversight and risk mitigation in AI-generated outputs, particularly in critical infrastructure and software systems.
These policies make it clear. AI cannot be a black box. Developers and security leaders must be able to explain, audit, and validate what AI creates. The burden is even greater for companies operating globally. Failure to implement oversight may lead to vulnerabilities and regulatory violations.
While AI is powerful, the human developer is still the ultimate arbiter of quality and safety. That means organizations must invest in training that teaches developers how to work with AI, not just beside it.
This includes:
- Understanding how GenAI models are trained—and their limitations.
- Spotting common security issues in AI-suggested code
- Practicing defensive programming and critical evaluation of suggestions
- Embedding security champions within development teams to coach and review GenAI usage.
We’re not trying to slow progress — we’re trying to steer it. Innovation without security is just acceleration toward risk.
Final Thought: GenAI is a Tool, Not a Teammate
The conversation around GenAI and security can’t wait. Ransomware actors and cybercriminals are adding AI to their tactics and arsenals. And many developers trust these tools without questioning their outcomes, unaware they can import the same vulnerabilities attackers are actively scanning.
It’s time to shift our mindset. GenAI isn’t a teammate; it’s a tool. Like any tool, it can be misused, misconfigured, or misunderstood.
Security remains a priority in software development lifecycles. Therefore, integrating oversight, education and security checkpoints into AI-assisted workflows can ensure that GenAI’s promise doesn’t come at the cost of safety.
