World Solve

Pirated or counterfeit software remains a common vector for malware distribution, and current detection and prevention methods haven't fully closed this persistent security gap. This continues to put both individual users and organizations at unnecessary risk.

As AI-generated faces and voices become more realistic, some biometric security systems can potentially be fooled by convincing synthetic versions of a real person's face or voice. Strengthening these systems against this emerging threat remains an active challenge.

Even with extensive automated testing, software products regularly ship with bugs that only appear once real users start interacting with them in unpredictable ways. Comprehensive, fully reliable bug detection before release remains an unsolved engineering challenge.

AI-generated contracts, court filings, or legal notices can appear authentic enough to deceive people unfamiliar with proper legal formatting, creating new fraud risks. Detecting these fakes reliably remains an emerging unresolved challenge.

Automated trading systems can execute manipulative strategies at speeds far beyond human traders, and regulators still struggle to detect this kind of abuse in real time. Closing this detection gap remains an urgent unresolved challenge for financial oversight.

Tools designed to detect AI-written assignments frequently produce false positives, wrongly flagging legitimate student work as AI-generated. This unreliability creates real consequences for students while genuine cheating sometimes slips through undetected.

Fake reviews generated at scale by AI can be nearly indistinguishable from genuine customer feedback, undermining trust in online shopping decisions. Detection tools are struggling to keep pace with how convincing these fakes have become.

AI-powered chatbots can now conduct long, personalized, convincing conversations designed to manipulate victims into financial scams, operating at a scale no human scammer could match. Detecting and stopping this abuse remains an evolving challenge.

Advanced spyware can be installed on phones without any visible sign, and even security experts sometimes struggle to detect its presence through standard tools. Better, more accessible detection methods remain an urgent unmet need.

Fake or substandard electronic parts sometimes slip into supply chains for critical equipment, including medical devices and vehicles, and current detection methods are neither fast nor cheap enough to catch every case. This creates hidden safety risks across many industries.

AI systems can generate false information with the same fluent, confident tone as accurate information, making it hard for users to tell the difference without independently verifying every claim. Reducing this so-called hallucination problem remains a central AI research challenge.

Zero-day flaws are unknown weaknesses in software that attackers can exploit before a fix exists, and current detection methods still miss many of them until after damage is done. Finding these flaws proactively remains one of cybersecurity's hardest problems.

A few seconds of someone's voice is now enough to generate convincing fake audio of them saying anything, and current detection tools often fail to catch high-quality fakes in real time. This is already being used for financial scams targeting families and businesses.

Deforestation, wildlife trade, and dense urbanisation increase contact between humans and animal reservoirs, enabling novel pathogens to emerge. Planetary-health surveillance systems and adaptable vaccine platforms will be essential.

Advances in synthetic biology may blur the line between accidental and deliberate outbreaks. Genomic anomaly detection and secure reporting pipelines could help.

Hybrid warfare tactics, including deepfakes and targeted propaganda, erode trust and polarise societies. Detection and provenance tools for media and secure civic-information channels are urgently needed.

Hybrid warfare tactics, including deepfakes and targeted propaganda, erode trust and polarise societies. Detection and provenance tools for media and secure civic-information channels are urgently needed.

Astronomical observations demand extra unseen mass, but candidate particles from extensions of the Standard Model remain undetected. New detector designs, astrophysical surveys, and theoretical models are needed to pin down its nature.

We have detected a handful of mergers, but their cosmic history and diversity remain unclear. Next-generation detectors with wider frequency coverage can map the gravitational-wave background.

Grand unified theories predict proton decay, yet experiments have not observed it. Larger underground detectors and longer observation times are needed.