AI is transforming industries – from recruitment and legal services to finance and healthcare. But, as adoption accelerates, many organizations are overlooking a critical step: security testing.
The Hidden Risks of Custom AI Agents
Custom AI agents, especially those built on large language models (LLMs), are not just smart – they’re also vulnerable. Unlike traditional software, these models can be manipulated in ways that are difficult to detect. Techniques like prompt injection and jailbreaking allow attackers to embed malicious instructions in everyday inputs.
Imagine an AI agent retrieving an email for a recruiter. Hidden within that email could be obfuscated commands that trick the model into exposing confidential candidate data – like salary expectations or personal contact details. In legal settings, an AI assistant might be used to summarize case files. But if it’s not properly secured, it could be manipulated to leak sensitive client information or internal strategy documents.
Real-World Examples Across Industries
- Recruitment: AI tools used to screen CVs or communicate with candidates can be compromised to extract private data or manipulate hiring decisions.
- Legal Services & Barristers’ Chambers: AI agents summarising case law or drafting briefs may inadvertently expose privileged information if they’re not tested for vulnerabilities.
- Other Sectors: Any business using AI to automate tasks – from customer service to financial analysis – is at risk if those agents aren’t properly secured.
Opensource Warning
Many open-source models and APIs – like EB2, Gemini, and Olam – are being deployed without proper authentication. This means threat actors can query backend systems, such as SQL databases, without restriction. These vulnerabilities are not theoretical – they’re happening now.
Security by Design: A Boardroom Priority
Business leaders often prioritise speed and profitability. But deploying AI without security testing is like launching a product without quality control. The result? Data breaches, reputational damage and regulatory consequences.
Testing your AI agents isn’t just a technical checkbox, it’s a strategic safeguard.
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