Securing AI on the Battlefield: DARPA’s SABER Initiative
In an era where artificial intelligence (AI) is becoming an integral part of military operations, ensuring its security and resilience is paramount. The Defense Advanced Research Projects Agency (DARPA) has launched the Securing Artificial Intelligence for Battlefield Effective Robustness (SABER) program to address AI vulnerabilities in military applications. As AI-supported battlefield systems grow in complexity, the risks posed by adversarial attacks, cyber threats, and electronic warfare (EW) cannot be ignored. SABER aims to strengthen AI’s defense by developing advanced red teaming frameworks and operational testing methodologies.
The Need for AI Security in Military Systems
AI is transforming modern warfare, providing enhanced decision-making, autonomous operations, and faster response times. Despite rapid advancements in AI with the potential to advance DoD warfighting, research at the intersection of computer security and AI has demonstrated the vulnerability of AI models. The nature of AI models makes them prone to errors that could be exploited with attacks such as data poisoning, adversarial manipulation, and other cyber exploits. Without rigorous testing and red teaming strategies, deploying state-of-the-art AI systems in contested environments could introduce significant operational risks.
To de-risk the use of AI on the battlefield, SABER is designed to conduct operationally realistic AI operational test and evaluation exercises (OT&E) in way that mirrors real-world military environments. The program seeks to assemble an operational AI red teaming ecosystem of technology, processes, places, and people necessary to secure AI-enabled battlefield systems for warfighter use.
How SABER Works: A Phased Approach
DARPA’s SABER initiative will execute its objectives through a structured 24-month program consisting of AI security OT&E exercises, referred to as SABER-OpX. These exercises will involve three technical teams (TTs):
- TT1: SABER Research Performers (soliciting for submissions):
- 1 Attack Techniques and Tools: Support the AI red team through the survey, testing, development, and use of AI attack techniques and red teaming tools.
- 2 Toolkit Integration: Integrate the resulting data into an operational AI red teaming framework and toolkit.
- TT2: Research Participant (information only) – Provide access to and use of existing AI-supported battlefield systems and support for experimental operational testing. Each SABER-OpX team will focus on near-term discriminative AI-enabled battlefield systems provided by TT2.
- TT3: Government Participant (information only) – May be comprised of University-affiliated Research Centers, federally funded Research and Development Centers, Government subject matter experts, and National Laboratories. Working with TT1, TT3 performers will develop counter-AI techniques and tools, develop new AI kill chain TTPs, and provide feedback on operations. Provide planning and logistical support for each SABER-OpX and streamline the lessons learned for dissemination to support technology transition.
Proposers may submit a single abstract proposal in response to TT1.1 or TT1.2, but not both. DARPA anticipates multiple awards for TT1.1 and a single award for TT1.2.
Key Dates:
- Proposal Abstract due date: March 31, 2025 at 5:00PM ET.
- Question submittal closed: March 31, 2025 at 5:00 PM ET.
- Notification of Intent to Prepare Classified Proposal: April 10, 2025 at 5:00PM ET.
- Proposal due date: May 06, 2025 05:00PM ET.
The Future of AI Security in Military Operations
SABER represents a significant leap in AI security for defense applications. By pioneering real-world adversarial testing and developing counter-AI strategies, DARPA is laying the groundwork for future AI-enabled warfare. The program’s success will not only enhance the security of military AI systems but also set a new standard for AI resilience in high-stakes environments. Through SABER, DARPA is ensuring that the U.S. military remains at the forefront of AI innovation.
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