Artificial intelligence has captured global attention with its groundbreaking potential, but like any powerful technology, it comes with dual sides. While AI is revolutionizing human life, it also carries significant security risks, particularly in the field of deep learning, which has become a critical weakness in AI systems.
Despite the remarkable changes AI brings to our daily lives, many people overlook the fact that deep learning—its core technology—faces numerous threats. As these applications grow more widespread, new security challenges are emerging, prompting urgent discussions about AI safety.
Recently, the 360 Security Research Institute released the "AI Security Risk White Paper," based on a year-long investigation into deep learning system vulnerabilities. The report highlights several key risks, such as software implementation flaws in deep learning frameworks, malicious sample generation targeting machine learning models, and data poisoning during training. These issues can cause AI-driven systems to misidentify objects, fail to detect threats, or even crash or be hijacked, turning smart devices into tools for cyberattacks.
While AI technologies like Google's AlphaGo have amazed the world with their computational power and strategic logic, it's important to note that such systems operate in controlled environments with limited external interaction, making them less vulnerable to real-world attacks. However, other AI applications often assume benign or closed scenarios, such as speech recognition using natural inputs or image recognition from standard photos. These assumptions ignore the possibility of malicious input, leaving gaps in security.
Take handwritten digit recognition, for example—a classic use case in deep learning. Systems trained on datasets like MNIST focus on classification accuracy and confidence levels, but they often neglect the possibility of adversarial inputs. This oversight creates a blind spot in AI security, where an attacker could manipulate the input to cause system failures or take control.
Currently, most deep learning software is built using frameworks that allow developers to create and train neural networks easily. However, the complexity of these systems also introduces hidden risks. If the framework or any of its dependencies contain vulnerabilities, the entire application is at risk. Additionally, modules developed by different teams may have inconsistent interfaces, leading to confusion and potential security flaws that are difficult to trace.
The 360 Seri0us team recently discovered dozens of vulnerabilities in deep learning frameworks and their dependent libraries within just one month. These include common issues such as memory access violations, pointer dereferences, integer overflows, and division-by-zero errors. If exploited, these flaws could lead to denial-of-service attacks, system crashes, or even data contamination.
As AI continues to evolve, addressing these security risks is crucial. We must not only advance AI technology but also ensure it is safe and reliable. As China’s largest internet security company, 360 is committed to helping the industry tackle AI security challenges and improve the overall safety of the AI ecosystem. By doing so, we can ensure that AI truly serves humanity in a secure and beneficial way.
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