Your business is only as safe as your dataset - and it might be poisoned.

Neutralize poisoned Datasets with Antidote

Antidote is a specialized AI system, built to detect and neutralise even the most invisible threats lurking in your training data. Apply now or continue reading.

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AI models are powerful yet dangerously trusting - that's their Blindspot

AI models rely on Humans, both at deployment and development to tell them what to do. They lack the capacity - unlike us - to understand things contextually. To them, all is probabilistic.

Training an AI model is much like training a dog, they rely on their trainer and environment to understand what's "good" and "bad"

Much like a dog, even though there are several methods to train them we most often use reinforcement training. Positive or negative stimuli is used to teach it (dog or AI) which behaviours are desired (good) or undesired (bad). Imagine now that, behind your back, someone's been mistraining your dog - worse even, they've also tampered with its training grounds.

Malicious actors modify training sets in a way that's imperceptible to you - your Blindspot.

It's exactly this Blindspot that attackers will take advantage of as though your model can't see in the traditional sense, it can detect these changes and it change its interpretation of what it "sees".

What happens then? When the "wrong" behaviours are the ones being reinforced? Your dog might learn to bite the mailman instead of guarding the perimeter;

Your AI alarm system might allow group of robbers in, mistaking them for you.

The Poison is spreading and the Pain is already being felt in AI cybersecurity

Enterprises already feel the pain

Average breach cost was US $4.8 M;

73 % of enterprises suffered at least one AI-related security incident in the last 12 months;

Data Poisoning is the fastest-growing attack class

Training-data poisoning now accounts for 23 % of all AI-security incidents, #2 after prompt-injection;

Gartner predicts 30 % of AI cyber-attacks will leverage training-data poisoning, model theft or adversarial samples by 2026

A little poison, very big effect

Altering just 1 – 3 % of a training set can cripple a model’s accuracy

In a medical-LLM study, replacing 0.001 % of tokens with misinformation pushed harmful completions up 7 – 11 %

An insidious and costly threat

EU & US regulators have already levied €287 M + US $412 M in AI-security fines in 2025; 76 % cited weak controls over training data or outputs

Average time to detect a poisoning attack: 248 days (vs 96 h for prompt injection)

What's Next?

We are working toward helping the sector secure itself. Currently seeking to validate the market while further developing our Proof-of-Concept, that boasts a 98% accuracy rate, into a Full stack Integrity Platform for AI.

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01. Market Fit &  Investor Relations

Open to meeting with Investors about accelerating our process. Wanting to anticipate markets needs so that we can better position ourselves to offer a premium solution.

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02. Early Users & QA

Open to matching companies interested in becoming Early Adopters and signing Letters of Intention or trying our product at a discounted rate in exchange for Detailed Feedback.

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03. Research & Development

We're researching and developing Label distribution checks. We want to increase supported model types  (text, audio, etc), create a signature library of known poisoning campaigns, and clear dashboards for confusion matrices, PR curves and attack impact.