The hidden threats in your AI Chatbot: What fintech companies must Know

By Ananda Krishna, Co-Founder & CTO of Astra Security

When a prominent digital lending firm noticed an unusual pattern of loan approvals, it initially seemed like a routine glitch. But deeper investigation revealed that attackers had exploited the company’s AI chatbot using crafted language inputs to bypass fraud checks. In a similar real-world case, a UK fintech startup lost £1.8 million in 2023 after hackers manipulated their chatbot to trigger unauthorized fund transfers, leading to both financial loss and regulatory scrutiny.

This incident is far from unique. As AI chatbots become embedded in core financial functions from onboarding and credit scoring to transaction processing and compliance, they also introduce a new layer of risk. These systems offer speed and scalability, but they also create new vulnerabilities that conventional security models often fail to detect.
To safeguard trust, compliance, and operational integrity, fintech organizations must rethink how they secure conversational AI.

Below are ten critical areas of focus to strengthen the resilience and security of AI chatbot deployments:

1. Guard Against Training Data Poisoning
AI models are only as reliable as the data they’re trained on, and attackers know it. In fintech, even 0.5% of poisoned data can reduce fraud detection accuracy by up to 34%, allowing high-risk transactions to go unnoticed. For example, an attacker could submit multiple small fraudulent transactions during a cashback promo, carefully mimicking legitimate behavior so the model learns to treat similar fraud as normal in future retraining cycles. Ongoing data validation, strict provenance tracking, and disciplined retraining are essential to defend against this silent but powerful threat.

2. Watch for Model Inversion and Data Reconstruction
Every response a chatbot gives can unintentionally leak insights about its training data or internal logic. With carefully crafted queries, attackers can reverse-engineer proprietary systems or even extract fragments of sensitive customer data. For instance, someone might ask: “What income is needed for a $250,000 loan?”, then slightly vary the question to reconstruct internal credit scoring thresholds over time. A real-world case like CVE-2023-34094, where a misconfigured ChuanghuChatGPT instance exposed config.json, highlights the risks of logic and data leakage. Defenses like response throttling, output obfuscation, and privacy-preserving model architectures are essential.

3. Spot and Stop Adversarial Inputs
Not all threats are obvious. Adversarial inputs, subtly altered prompts or documents crafted to confuse AI models can trigger risky decisions without raising alarms. In fintech, a loan applicant might rephrase “currently unemployed” as “in between roles” or tweak a scanned ID to bypass OCR checks. These manipulations often look harmless to humans but can mislead AI systems.
A related risk was seen in CVE-2023-5204 where an unauthenticated SQL injection flaw in an AI chatbot plugin allowed attackers to alter backend queries, effectively injecting malicious behavior into financial workflows. Mitigating this threat requires adversarial training, input normalization, and multi-channel verification to ensure inputs are genuine and consistent.

4. Block Prompt Injection Before It Bypasses Controls
Prompt injection is a critical and often overlooked threat where attackers embed hidden commands in user inputs to hijack chatbot behavior, e.g., “ignore security checks” or “approve this transaction no matter what.” A notable case is CVE-2025-43714, where SVG rendering led to HTML injection in ChatGPT environments, showing how even visual content can enable unauthorized code execution. Mitigate this risk through strict separation of user/system prompts, robust intent validation, and layered security controls that prevent the model from being manipulated.

5. Lock Down Third-Party API Access
AI chatbots depend on external APIs for payments, identity checks, and data retrieval but these can become attack vectors if not secured. CVE-2024-27564 exposed a Server-Side Request Forgery (SSRF) vulnerability in ChatGPT, where lack of proper input sanitization let attackers access internal resources. Secure all API connections with strong authentication, rate limiting, and continuous monitoring to prevent data leaks and unauthorized access.

6. Stay Ahead of Regulatory Compliance
Financial AI chatbots operate in a tightly regulated landscape, think GDPR, PCI-DSS, the EU AI Act, and NIST’s AI Risk Management Framework. Any gaps in data privacy, audit trails, or explainability can lead to costly fines and legal challenges. Staying compliant means building transparency into every interaction: traceable decision logs, clear consent management, and robust data governance frameworks. Regulatory readiness isn’t optional, it’s a must-have for sustainable fintech innovation.

7. Detect Behavioral Anomalies in Real Time
Static testing can only catch so much. To truly defend against evolving threats, continuous real-time monitoring of chatbot behavior is essential. Unusual language patterns, unexpected decision shifts, or spikes in false positives can be early warning signs of an attack in progress. Setting up automated alerts and human oversight protocols helps catch these red flags before they escalate into serious breaches.

8. Make Red-Teaming and Ethical Hacking Your AI’s Stress Test
Think of red-teaming and ethical hacking as a fire drill for your AI chatbot. Regularly putting your system under pressure, simulating crafty prompt injections, model theft attempts, and other sneaky attacks exposes weak spots before the real threats arrive. This isn’t a one-time exercise; it’s a continuous commitment to resilience, ensuring your defenses evolve alongside attackers’ ever-changing playbooks.

9. Turn Security into Everyone’s Business
Security can’t live in a silo. It’s a shared responsibility that spans from product strategists and compliance gurus to developers and frontline support staff. Building a culture where everyone understands the unique risks of conversational AI, through clear protocols, regular training, and well-defined escalation routes, creates an all-hands-on-deck defense. When the whole team is alert, vulnerabilities shrink and response times accelerate.

10. Design AI to Bounce Back — Not Just Speed Ahead
In fintech, lightning-fast AI responses are impressive but they shouldn’t come at the cost of safety. The smartest AI systems are built for resilience, capable of spotting manipulation attempts early and recovering quickly without missing a beat. This means layering your defenses with backup plans, weaving in human oversight where needed, and embedding multiple checkpoints that keep your AI honest, even when the unexpected strikes.

As AI chatbots rapidly transform fintech, it’s clear they’re far more than just convenient tools, they’re the brains behind critical financial decisions. But with great power comes great risk. Left unprotected, these systems become prime targets for sophisticated attacks that can ripple through every layer of a business from compliance headaches and operational disruptions to shattered customer trust.

Securing AI chatbots isn’t just a technical checkbox, it’s a vital business imperative. Those who proactively uncover and neutralize these hidden threats don’t just protect their current

operations; they future-proof their organizations against the ever-evolving tactics of cyber adversaries. In today’s fast-paced digital world, resilience isn’t just an advantage; it’s the foundation for lasting success.