Identifying the evolving security threats to AI models
Artificial intelligence (AI) has quickly become a cornerstone of technological and business innovation, permeating every sector and fundamentally transforming the way 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 could include data manipulation, hostile attacks, and exploitation of machine learning models, each of which has serious potential implications for privacy, security, and trust.
As AI becomes 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 staying vigilant and continually identifying and addressing these vulnerabilities, companies can protect not only their AI systems, but also the integrity and resilience of their broader digital environment.
Lead security researcher at HiddenLayer.
The new threats facing AI models and users
As the use of AI increases, so does the complexity of the threats it faces. Some of the most pressing threats include trust in digital content, backdoors intentionally or unintentionally embedded in models, traditional security holes exploited by attackers, and new techniques that cleverly bypass existing protections. Additionally, the rise of deepfakes and synthetic media further complicates the landscape, creating challenges around verifying the authenticity and integrity of AI-generated content.
Trust 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 protections? For example, watermark manipulation allows adversaries to tamper with the authenticity of images generated by AI models. This technique can add or remove invisible watermarks that mark content as AI-generated, undermining trust in the content and promoting 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, an oft-reused model with a backdoor could have serious supply chain implications. An advanced method developed by our Synaptic Adversarial Intelligence (SAI) team, called “ShadowLogic,” allows adversaries to implant codeless, hidden backdoors into neural network models on any modality. By manipulating the model’s computational graph, attackers can compromise its integrity without detection, allowing the backdoor to persist even when a model is fine-tuned.
Integrating AI into high-impact technologies: AI models such as Google’s Gemini have proven to be susceptible to indirect prompt injection attacks. Under certain circumstances, attackers can manipulate these models to produce misleading or malicious responses, and even cause them to call APIs, highlighting the continued need for vigilant defenses.
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 techniquesWhile traditional security vulnerabilities still pose a major threat to the AI ecosystem, new attack techniques emerge almost daily. Techniques such as Knowledge Return Oriented Prompting (KROP), developed by HiddenLayer’s SAI team, pose a significant challenge to AI safety. 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 bad actors might use – often before those adversaries even recognize the potential opportunities for impact. By combining proactive research with innovative, automated tools designed to expose hidden vulnerabilities within AI frameworks, researchers can discover and reveal 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 creating baseline protections to combat both known and emerging threats.
Identifying vulnerabilities is just the first step. It is equally important to translate academic research into practical, deployable solutions that work effectively in real production environments. This bridge from theory to application is illustrated in projects where HiddenLayer’s SAI team has adapted academic insights to address real-world security risks, underscoring the importance of making research actionable and ensuring defenses are robust, scalable and adaptable to evolving threats. By turning fundamental research into operational defenses, the industry not only protects AI systems, but also builds resilience and trust in AI-driven innovation, protecting both users and organizations from a rapidly changing threat landscape. This proactive, layered approach is essential for enabling secure, reliable AI applications that can withstand both current and future adversarial techniques.
Innovating towards more secure 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 the methods and motives of attackers. Threat actors are increasingly focused on exploiting weaknesses specific to AI models, from adversarial attacks that manipulate model output to data poisoning techniques that compromise model accuracy. To address these risks, the industry is shifting to embedding security directly into the development and deployment phases of AI, making it an integral part of the AI lifecycle. This proactive approach promotes safer environments for AI and mitigates risks before they manifest, reducing the chance of unexpected disruptions.
Researchers and industry leaders alike are accelerating their efforts to identify and counter evolving vulnerabilities. As AI research migrates from theoretical exploration to practical application, new attack methods are quickly shifting from academic discourse to real-world implementation. Adopting “secure by design” principles is essential to creating a “security first” mentality, which, while not foolproof, elevates basic protections for AI systems and the industries that depend on them. As AI revolutionizes industries from healthcare to finance, embedding robust security measures is critical to supporting sustainable growth and driving 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 advanced threats they face, paving the way for future developments that are both innovative and safe.
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