AI Security & Red Teaming for Agriculture
Food and agriculture is now designated critical infrastructure, and its AI attack surface is growing: models steering irrigation and inputs, agents touching ERP and logistics systems, and assistants with access to grower data and pricing. AI security and red teaming for agriculture means adversarially testing those systems before someone else does. Can a poisoned sensor feed skew input recommendations? Can a prompt injection pull confidential grower records or contract pricing out of an assistant? Can a manipulated model corrupt the FSMA traceability data your recalls depend on? We attack your AI the way a real adversary would, then help you close what we find.
AI Security & Red Teaming, built for agriculture
We scope the engagement around your real exposure: decision models, agents with system access, grower-facing assistants, and the data pipelines feeding them.
We run adversarial testing against each target: prompt injection, data poisoning scenarios, model manipulation, and attempts to extract grower and pricing data.
We test the integration seams, where an agent's permissions into ERP, farm management, or logistics systems can be abused, not just the model itself.
We deliver findings ranked by exploitability and business impact, with concrete fixes, then retest to confirm the holes are closed.
Where it pays off in agriculture
Decision model integrity
Test whether corrupted sensor or imagery inputs can skew yield, irrigation, or input models enough to cause real losses.
Grower data extraction
Attempt to pull confidential farm records, contract terms, and pricing out of assistants and agents through adversarial prompting.
Traceability tampering
Probe whether the AI-fed records behind FSMA traceability and recall response can be silently corrupted.
Agent privilege abuse
Attack the permissions your automation holds in ERP and logistics systems to find what a hijacked agent could actually do.
Engagements typically surface a handful of high-impact findings, most commonly injection paths to grower data and over-permissioned agents, and clients close them before harvest-season stakes make an incident unaffordable.
Agriculture AI, answered
Yes, and increasingly so. Food and agriculture is critical infrastructure, farm data has market value, and AI now sits in decision paths that affect food safety records and grower trust. Testing before deployment is far cheaper than a corrupted season or a data breach disclosure.
Whatever AI you run: models influencing agronomy decisions, agents connected to ERP and logistics, and assistants with access to grower or pricing data. We test the models, the prompts, and critically the integration seams where permissions can be abused.
No. We test against staging environments or tightly scoped production windows agreed with your team, and destructive scenarios run only in isolation. The point is to find failures before the field does, not to cause one.
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Bring AI Security & Red Teaming to your agriculture team
Book a free consultation. We'll show you the highest-leverage place to start and exactly how we'd ship it.