The landscape of application security has undergone significant transformations over the last couple of decades. In the early 2000s, challenges such as SQL injection and Cross-Site Scripting (XSS) attacks kept cybersecurity teams on their toes as these threats exploited vulnerabilities in web applications beyond traditional network firewalls. Responding to these challenges led to the adoption of measures like web application firewalls (WAF) and rigorous source code security reviews. 2. DevSecOps and the New Norms Fast-forward to today, the era of DevSecOps has ushered in a new standard. Automated checks within CI/CD pipelines, incorporating dynamic application security testing (DAST) and static application security testing (SAST), have become the norm. 3. Emerging Concerns: Attacks on AI and Machine Learning Systems While the industry celebrates the widespread adoption of AI and machine learning, a new threat is looming—one that has the potential to be as elusive as the challenges posed by SQL injections in the past. These are attacks specifically targeting artificial intelligence and machine learning systems. 4. Understanding AI as a Blind Spot in Cybersecurity Unlike traditional cybersecurity processes focused on infrastructure hardening, patching, and vulnerability assessments, attacks on AI involve a different breed of challenges. Traditional assurance processes may not cover AI-specific attacks like data poisoning, membership inference, and model evasion. 5. Common AI Attacks: Inference, Evasion, and Poisoning
Inference Attacks: Attackers aim to unravel the inner workings of a model during inference attacks. By analyzing responses from machine learning models, attackers can reconstruct training data, potentially exposing sensitive information. Poisoning Attacks: This attack occurs at the training data level, where attackers manipulate data to tamper with a model's decision-making. Compromising data repositories used for training can have widespread consequences. Evasion Attacks: Evasion attacks involve tricking models by subtly altering data. Small changes in input, not noticeable to humans, can lead to dramatically different decisions by machine learning models, posing risks in various sectors.
6. Strengthening AI Applications Against Attacks
Addressing these challenges requires a proactive and multi-faceted approach:
Threat Modeling for Machine Learning Models: Before choosing or deploying any new or pre-built model, conducting thorough threat modeling should be a standard practice.
Enhanced Detection Controls: Update detection controls to alert against repeated queries, indicative of a potential inference attack on a specific machine learning API.
Model Hardening: Implement measures to sanitize confidence scores in model responses, balancing usability with security.
Adversarial Sample Training: Train machine learning models on adversarial samples to assess their resilience to attacks.
Securing Data Stores: Subject data stores used for model training to rigorous security testing to prevent vulnerabilities and regularly verify model performance and data integrity.
Data Source Policies: Establish policies regarding the use of public or open-source data for training, acknowledging the convenience but also potential risks associated with compromises to data integrity.
As AI-based attacks continue to rise, it is crucial for cybersecurity teams to elevate their understanding and awareness of these evolving threats. Implementing robust technical controls and governance is imperative to prevent potential havoc, reminiscent of challenges posed by SQL injections in the past.
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