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Artificial Intelligence (AI) has rapidly evolved into a cornerstone of technological and business innovation, permeating every sector and fundamentally transforming how we interact with the world. AI tools now streamline decision-making, optimize operations, and enable new personalized experiences.
However, this rapid expansion brings with it a complex and growing threat landscape – one that combines traditional cybersecurity risks with unique vulnerabilities specific to AI. These emerging risks can include data manipulation, adversary attacks and exploitation of machine learning models, each of which poses a serious potential impact on privacy, security and trust.
As AI continues to be deeply integrated into critical infrastructures, from healthcare and finance to national security, it is crucial for organizations to adopt a proactive and layered defense strategy. By remaining vigilant and continuously identifying and addressing these vulnerabilities, companies can protect not only their AI systems, but also the integrity and resilience of their broader digital environments.
Principal Security Researcher at HiddenLayer.
New threats to AI models and users
As the use of AI expands, so does the complexity of the threats it faces. Some of the most pressing threats involve trust in digital content, backdoors intentionally or unintentionally embedded in models, traditional security gaps exploited by attackers, and new techniques that cleverly bypass existing safeguards. Additionally, the rise of deepfakes and synthetic media further complicates the landscape, creating challenges around verifying authenticity and integrity in AI-generated content.
Trust in digital content: As AI-generated content slowly becomes indistinguishable from real images, companies are building safeguards to stop the spread of misinformation. What happens if a vulnerability is found in one of these safeguards? Watermark manipulation, for example, allows adversaries to tamper with the authenticity of images generated by AI models. This technique can add or remove invisible watermarks that mark the content generated by AI, undermining trust in the content and encouraging misinformation – a scenario that can lead to serious social ramifications.
Backdoors in models: Because of open source nature of AI models through sites like Hugging Face, an often reused model containing a backdoor could lead to serious supply chain implications. A cutting-edge method developed by our Synaptic Adversarial Intelligence (SAI) team, dubbed “ShadowLogic”, allows adversaries to implant uncoded and hidden backdoors into neural network models in any mode. By manipulating the computational graph of the model, attackers can compromise its integrity without detection, persisting the backdoor even when a model is fine tuned.
Integrating AI into High Impact Technologies: AI models like GoogleThe Geminis have shown themselves to be susceptible to indirect immediate injection attacks. Under certain conditions, attackers can manipulate these models to produce misleading or malicious responses, and even trigger API calls, highlighting the ongoing need for vigilant defense mechanisms.
Traditional security vulnerabilities: Common vulnerabilities and exposures (CVEs) in AI infrastructure continue to plague organizations. Attackers often exploit weaknesses in open-source frameworks, making it essential to proactively identify and address these vulnerabilities.
New attack techniques: While traditional security vulnerabilities still pose a major threat to the AI ecosystem, new attack techniques are emerging almost every day. Techniques such as KROP (Knowledge Return Oriented Prompting), developed by HiddenLayer’s SAI team, present a significant challenge to AI security. These new methods allow adversaries to bypass conventional security measures built into large language models (LLM), opening the door to unintended consequences.
Identify vulnerabilities before adversaries do
To combat these threats, researchers must stay one step ahead, anticipating the techniques that bad actors may employ—often before those adversaries even recognize the potential opportunity for impact. By combining proactive research with innovative, automated tools designed to expose hidden vulnerabilities in AI frameworks, researchers can discover and disclose new Common Vulnerabilities and Exposures (CVEs). This responsible approach to vulnerability disclosure not only strengthens individual AI systems, but also fortifies the broader industry by raising awareness and establishing baseline protections to combat known and emerging threats.
Identifying vulnerabilities is only the first step. It is equally critical to translate academic research into practical, implementable solutions that operate effectively in real production settings. This bridge from theory to application is exemplified in projects where HiddenLayer’s SAI team has adapted academic insights to address current security risks, emphasizing the importance of making research actionable, and ensuring that defenses are robust, scalable and adaptable to evolving threats. By turning foundational research into operational defenses, the industry not only protects AI systems, but also builds resilience and trust in AI-driven innovation, safeguarding users and organizations alike against ‘to a rapidly changing threat landscape. This proactive and layered approach is essential to enabling secure and reliable AI applications that can withstand current and future adversary techniques.
Innovate towards safer AI systems
Security around AI systems can no longer be an afterthought; it must be woven into the fabric of AI innovation. As AI technologies advance, so do the methods and motives of attackers. Threat actors are increasingly focused on exploiting specific weaknesses in AI models, from adversarial attacks that manipulate model outputs to data poisoning techniques that degrade model accuracy. To address these risks, the industry is moving toward embedding security directly into the development and implementation phases of AI, making it an integral part of the AI lifecycle. This proactive approach fosters safer environments for AI and mitigates risks before they occur, reducing the likelihood of unexpected outages.
Researchers and industry leaders are accelerating efforts to identify and counter evolving vulnerabilities. As AI research migrates from theoretical exploration to practical application, new attack methods are rapidly moving from academic discourse to real-world implementation. Adopting “secure by design” principles is essential to establishing a security-first mindset, which, while not foolproof, raises the baseline protection for AI systems and the industries that depend on them. As AI revolutionizes sectors from healthcare to finance, embedding robust security measures is vital to support sustainable growth and foster trust in these transformative technologies. Using security not as a barrier, but as a catalyst for responsible progress will ensure that AI systems are resilient, reliable and equipped to withstand the dynamic and sophisticated threats they face, paving the way for future advancements that they are both innovative and safe.
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