AI platforms show promise in steering people away from potentially illegal wildlife purchases, but they are not reliable barriers. A 2026 study found that seven AI platforms performed better overall than a standard Google search in tests across 20 animal groups. The same tests also found that safeguards could be bypassed and that results varied by species. The study evaluated chatbot responses, not whether AI reduced real-world sales or trafficking.
What the 2026 study tested
In a study published online on 28 September 2026 in Conservation Biology, David L. Roberts and Jason R. C. Nurse tested seven AI platforms and a Google search using three prompts covering 20 taxa. They assessed responses for four themes: legality, conservation status, suggested substitutions and animal-welfare concerns. The paper is titled “Assessing safeguards against the illegal wildlife trade on AI platforms” (article e70400).
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The comparison concerns those particular prompts and platforms. The study does not establish that the services reduced purchases, prevented trafficking incidents or led to seizures.
What the results say—and what they do not
AI responses were better overall than standard search in the tests
Across the tested prompts, the AI platforms performed better overall than standard Google search at preventing potentially illegal wildlife trade. Gemini had the highest overall score, followed closely by DeepSeek, Grok and ChatGPT. The accessible study abstract does not give numerical platform scores, so the ordering should be read as a result of this evaluation—not a general or lasting ranking of AI services.
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Safeguards could be circumvented
A prompt designed to get around protections returned links to websites that might trade in illegal wildlife. The study describes potential trading sites; it does not establish that researchers verified completed sales. The finding is enough to show that a refusal or warning cannot be treated as proof that a platform will never assist a would-be buyer.
Responses differed by animal group
Safeguards appeared strongest for slow lorises (Nycticebus spp.), chimpanzees (Pan troglodytes) and tigers (Panthera tigris), and weaker for corals, tarantulas and orchids. Co-author David Roberts told the University of Kent that the weaker responses concerned less charismatic taxa that are still of equal concern. The authors suggest that differences in online information and wider social and political interests may help explain the pattern; the study does not establish those factors as causes.
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Legal information may help, but buyer behavior was not measured
The platforms also supplied information, particularly about legality, that could inform potential buyers. Co-author Jason Nurse described that as promising, while also noting the links to potential websites trading in possibly illegal wildlife. This supports the possibility that AI could help inform consumers; it is not evidence that anyone changed their plans or that demand fell.
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A chatbot responds to a person’s request. Listing moderation and image recognition instead aim to identify and block suspicious content on online marketplaces or platforms. These are distinct interventions, with different measures of success and different ways to fail: a chatbot guardrail can be bypassed, while listing systems can miss coded language, disguised images or species they do not recognize.
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WWF’s December 2025 account of the Coalition to End Wildlife Trafficking Online describes company efforts including language-based moderation, image recognition and volunteer reporting. WWF says the Coalition launched in 2018 with WWF, TRAFFIC and IFAW as conservation convenors, and had 41 company members across more than 50 digital platforms at the time of that account.
| Reported activity | Figure and scope | How to interpret it |
|---|---|---|
| Prohibited wildlife listings and suspected illicit sellers blocked by Coalition company members | 24.1 million since 2018, as reported for 2024 by WWF | Platform-reported blocking activity, not a count of verified trafficking cases prevented. |
| Prohibited wildlife listings and suspected illicit sellers blocked by Coalition company members | 63.3 million since 2018, at the close of 2025, according to WWF | A cumulative reported activity figure; it should not be combined with the chatbot study’s results. |
| Species recognized by AI Guardian of Endangered Species 2.0 | 34 species, reported by WWF in 2025 for the Baidu and IFAW system | A system capability figure, not a measure of trafficking prevented. |
| Average identification accuracy for AI Guardian 2.0 | 86%, reported by WWF in 2025 | A reported model metric; it does not by itself show real-world enforcement effectiveness. |
| Images screened, targeted wildlife-product images identified and illegal online postings removed by AI Guardian 2.0 | 430,000 screened; 40,000 targeted images identified; 15,000 postings removed, reported by WWF in 2025 | Reported screening and removal activity, distinct from verified cases or outcomes. |
WWF also describes eBay image-detection models for restricted wildlife products and the Wildlife Cyber Spotter Program, where trained volunteers identify suspicious listings and evasion tactics. WWF advises people who encounter suspicious listings to report them directly to the platform or to the Coalition.
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What stronger evidence would look like
Different claims about AI and wildlife trafficking require different evidence. A prompt test can show how a chatbot responds to a specific request; a model metric can show how a detection system performed on a defined task; a platform report can count moderation actions. None alone establishes that trafficking declined.
CITES requires Parties to submit annual illegal-trade reports covering seizures involving CITES-listed species, including domestic seizures. Its restricted-access Illegal Trade Database contains individual seizure data reported since 2016 and is available to verified government representatives and ICCWC partners. Seizure records can inform enforcement and policy, but they are not a complete count of illegal trade or online listings.
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Other technical and policy work addresses separate parts of the problem. A 2025 marketplace-classification paper by Barbosa and co-authors reports classifiers reaching up to 95% F1 score, using LLM-generated pseudo-labels to identify wildlife-trafficking advertisements at lower cost than direct LLM labeling. That result concerns identifying ads, not whether chatbots resist purchase requests.
A European Commission report published in June 2026 on an April workshop describes EU-funded work on illicit-goods detection, risk assessment and AI-driven monitoring. It identifies wildlife-specific challenges including species identification, fragmented legislation, gaps in expertise and capacity, implementation barriers, and limited awareness or political commitment. Its emphasis is on testing, training, harmonization and public-private partnerships—not a finding that a particular system has already stopped wildlife trafficking.
How to judge claims that AI is helping
- Identify where the system acts: on a consumer prompt, a marketplace listing, an image, or an official inspection.
- Check what was measured: prompt responses, model accuracy, moderation actions, seizures or another enforcement outcome are not interchangeable.
- Look for the stated failure modes: circumvention, coded terms or images, species-recognition gaps, and limited enforcement capacity can all leave openings.
- Ask who reports the result: a platform or coalition count should be attributed to the reporting organization; a model metric is not an independent estimate of cases prevented.
The available evidence makes AI a potentially useful part of a broader response, not a substitute for careful moderation, human reporting and enforcement. The 2026 chatbot study offers a reason for cautious optimism about some consumer-facing safeguards, alongside direct evidence that they can fail.
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