Questions about AI security have been buzzing in our Slack channels and governance meetings since reports emerged of threat actors manipulating AI systems like ChatGPT, Gemini, and Google AI Overview. These questions are direct and urgent. Here's what your validation and assurance teams need to know.
The Source of the Threat
Your AI chatbot pulls information from the internet. An attacker can publish optimized content with malicious links or false information, which your system might ingest during updates. This results in poisoned responses to your users.
This is no longer theoretical. Attackers target popular AI systems by exploiting how they prioritize web content. If your team manages customer-facing AI or integrates third-party models, you need answers now.
Can Attackers Really Influence My AI?
It's not as easy as just writing something online, but it's closer than you'd like.
Many AI systems use retrieval-augmented generation or incorporate recent web content to stay updated. Attackers use SEO techniques to ensure malicious content ranks highly when the AI searches for information. Your model doesn't recognize the content as malicious; it sees it as authoritative.
Start by understanding your model's information sources. Document every data pipeline feeding your system. If you're using a third-party API, ensure there's clear contractual language about their data sourcing and validation practices. For internal models, implement source reputation scoring before ingestion. ISO/IEC 5259 offers data quality frameworks you can adapt to assess source credibility.
Don't assume your vendor handles this. Ask them directly: "What controls prevent malicious content from influencing model responses?" If they can't answer specifically, that's your risk.
How Can We Detect If Our Models Are Compromised?
Continuous output monitoring with baseline drift detection is essential.
Establish response patterns for common queries during validation. Monitor for deviations. If your chatbot starts including unfamiliar URLs or if confidence scores drop for stable queries, investigate immediately.
Automate scanning for:
- New domains in responses
- Changes in response structure or length
- Unusual linguistic patterns
- Links to recently registered domains
NIST AI RMF's monitoring guidance emphasizes tracking "emergent properties and unexpected behaviors." Content poisoning qualifies. Your monitoring system should flag responses with URLs not on your approved list. Yes, you need an approved domain list.
Log everything. You can't investigate what you didn't capture. SR 11-7 requires ongoing model performance monitoring. Apply that rigor to content integrity.
Can Models Be Poisoned Between Validation Cycles?
Yes, and that's the issue with point-in-time validation.
If your model updates its knowledge base or uses a continuously updating third-party API, the attack surface changes constantly. An attacker can publish malicious content between validation runs.
This requires continuous validation, not quarterly audits. Implement daily spot checks on high-risk queries. For customer-facing systems, run automated test suites against known-good queries every time you update the model or its data sources.
ISO/IEC 42001's clause 8.2 requires you to "monitor, measure, analyze and evaluate" AI system performance. For systems exposed to dynamic data sources, that means continuous monitoring. Integrate it into your operations, not just audits.
Consider a validation tier system: critical queries daily, standard queries weekly, edge cases monthly. Prioritize based on business impact and manipulation likelihood.
Should We Block External URLs in Responses?
You can, but that's treating the symptom, not the disease.
Blocking URLs prevents direct phishing links but doesn't stop misinformation or biased responses. The attacker's content still influences what your model "learned" or retrieved. Your users get false information, just without a clickable link.
A better approach is response validation before delivery. Run outputs through a secondary check that:
- Verifies factual claims against trusted sources
- Checks URLs against threat intelligence feeds
- Flags low confidence scores for human review
- Compares responses to historical baselines for the same query
This acts as a control gate between model output and user delivery. It adds latency, but for high-stakes applications, that trade-off is necessary.
The EU AI Act's Article 15 on accuracy requirements applies here. If your system is high-risk under Annex III, you need "appropriate levels of accuracy" throughout the lifecycle. Poisoned outputs fail that standard.
Who's Responsible, Us or Our Model Provider?
Both, and you need it in writing.
Your vendor provides the model. You deploy it with your data sources for your use cases. Responsibility splits along that line.
Get contractual clarity on:
- Data sources they use and how they validate them
- Monitoring they provide and what you must implement
- Incident investigation responsibilities and access
- Notification timelines if they detect manipulation
Then own your side. You're responsible for:
- Model configuration
- Additional data you feed it
- Monitoring outputs in your environment
- Incident response when issues arise
SR 11-7 is explicit: the board and senior management own model risk, regardless of whether you built or bought the model. "We use a vendor" isn't a risk transfer strategy. Document your validation approach and your vendor's commitments. Your auditors will ask.
Is This Happening at Scale or Just an Isolated Incident?
Reports confirm it's targeting ChatGPT, Gemini, and Google AI Overview. This isn't one system or one vendor. It's a systematic attack pattern against multiple platforms.
Treat this as an established threat vector. Attackers understand how to manipulate AI systems through content poisoning. Your risk assessment should reflect that reality.
Update your threat model. Add "adversarial content injection via data poisoning" to your risk register if it's not already there. Assign it a likelihood based on your exposure (public-facing systems with web retrieval are high risk). Assign impact based on your use case (customer service chatbots spreading phishing links is high impact).
Then resource your controls accordingly. This isn't a future problem you can schedule for next quarter's roadmap.
Where to Go for More
Start with MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems). It catalogs AI-specific attack techniques, including data poisoning patterns. Use it to build threat scenarios specific to your architecture.
Review NIST AI 100-2 for adversarial machine learning taxonomy. It'll help you communicate these risks to stakeholders who don't specialize in AI security.
For governance integration, map these controls back to ISO/IEC 42001's clause 6.1.3 on risk assessment and 8.2 on monitoring. Show how content integrity monitoring fulfills your management system requirements.
And talk to your red team. Have them attempt content poisoning against your non-production systems. You need to know your actual resilience, not your theoretical resilience.
