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 are now streamlining decision-making, optimizing operations and enabling 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 new risks may include data manipulation, adversarial attacks, and the exploitation of machine learning models, each of which poses a serious potential impact on privacy, security, and trust.
As artificial intelligence continues to become deeply integrated into critical infrastructures, from healthcare and finance to national security, it is critical for organizations to adopt a proactive, 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 Scientist at HiddenLayer.
The new threats facing AI models and users
As the use of artificial intelligence 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 holes exploited by attackers, and new techniques that cleverly bypass existing security measures. In addition, the emergence of deepfakes and synthetic media further complicates the landscape, creating challenges around verifying the authenticity and integrity of 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 security measures? Watermark manipulation gives e.g. adversaries the possibility of tampering with the authenticity of images generated by AI models. This technique can add or remove invisible watermarks that mark content as AI-generated, undermine trust in the content and promote misinformation – a scenario that could lead to serious social consequences.
Rear doors in models: Due to the open source nature of AI models through sites like Hugging Face, a frequently reused model containing a backdoor can lead to serious supply chain implications. A breakthrough method developed by our Synaptic Adversarial Intelligence (SAI) team, called ‘ShadowLogic’, enables adversaries to implant codeless, hidden backdoors into neural network models across any modality. By manipulating the model’s computational graph, attackers can compromise its integrity without detection and continue the backdoor even when a model is fine-tuned.
Integrating artificial intelligence into high-impact technologies: AI models like Google’s Gemini have been shown to be susceptible to indirect, rapid injection attacks. Under certain conditions, attackers can manipulate these models to produce misleading or malicious responses and even cause them to call APIs, underscoring 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 important 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 an almost daily occurrence. Techniques such as Knowledge Return Oriented Prompting (KROP), developed by HiddenLayer’s SAI team, pose a significant challenge to AI security. These new methods allow adversaries to bypass conventional safeguards built into large language models (LLMs), opening the door to unintended consequences.
Identify vulnerabilities before adversaries do
To combat these threats, researchers must stay one step ahead and anticipate the techniques that bad actors might employ—often before those adversaries even recognize potential opportunities for influence. By combining proactive research with innovative, automated tools designed to reveal hidden vulnerabilities within AI frameworks, researchers can uncover and expose new Common Vulnerabilities and Exposures (CVEs). This responsible approach to vulnerability disclosure not only strengthens individual AI systems, but also strengthens the broader industry by raising awareness and establishing baseline protections to combat both known and emerging threats.
Identifying vulnerabilities is only the first step. It is equally important to translate academic research into practical, implementable solutions that work effectively in real production environments. This bridge from theory to application is exemplified in projects where HiddenLayer’s SAI team adapted academic insights to tackle actual security risks, underscoring the importance of making research actionable and ensuring defenses are robust, scalable and adaptable to threats that develops. By transforming basic research into operational defense, the industry not only protects AI systems, but also builds resilience and trust in AI-powered innovation, protecting both users and organizations from a rapidly changing threat landscape. This proactive, layered approach is critical to enabling secure, reliable AI applications that can withstand both current and future adversarial techniques.
Innovation 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 evolve, so do attackers’ methods and motives. Threat actors are increasingly focused on exploiting weaknesses specific to AI models, from adversarial attacks that manipulate model output to data poisoning techniques that degrade model accuracy. To address these risks, the industry is shifting towards integrating 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 artificial intelligence and mitigates risks before they manifest, reducing the likelihood of unexpected disruptions.
Researchers and industry leaders alike 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 critical to establishing a security-first mindset that, while not foolproof, raises the baseline protections for AI systems and the industries that rely on them. As artificial intelligence revolutionizes sectors from healthcare to finance, it is critical to integrate robust safeguards to support sustainable growth and build trust in these transformative technologies. Embracing 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 advances that are both innovative and safe.
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