Securing Artificial Intelligence for Battlefield Effective Robustness (SABER)
Closed Solicitation Posted
This opportunity was awarded. See its 2 award notices in the notice history.
- Solicitation number
- HR001125S0009
- Agency
- Defense Advanced Research Projects Agency Department of Defense
- Responses due
- Set-aside
- No set-aside
Opportunity facts
- NAICS code
- 541715 Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- Points of contact
-
- BAA Coordinator saber@darpa.mil
Notice details come from SAM.gov. Updated .
About this opportunity
The Defense Advanced Research Projects Agency (DARPA) is seeking to develop an advanced AI red team capability through the Securing Artificial Intelligence for Battlefield Effective Robustness (SABER) program. The solicitation aims to establish a sustainable model for operationally assessing vulnerabilities in AI-enabled autonomous ground and aerial military systems deployable within the next 1-3 years. Proposers must be U.S. organizations capable of handling classified information at the SECRET level, with at least three U.S. citizen key personnel holding SECRET clearances. The program will focus on developing counter-AI techniques and tools across physical, adversarial AI, cyber, and electronic warfare domains, with two technical teams: TT1.1 will develop AI attack effect techniques, while TT1.2 will integrate these into a unified operational framework. Success will be measured using an AI Red Team Effectiveness (ARTE) metric that evaluates performance, time, and cost of generating AI attack effects. Key proposal deadlines include an abstract due date of March 31, 2025, and a full proposal submission deadline of May 6, 2025.
The solicitation does not have a specific set-aside designation and anticipates multiple awards across two technical teams. The program is structured as a 24-month effort with two nine-month SABER-OpX exercises, each involving four week-long experiments to assess AI system vulnerabilities. While specific award values are not explicitly stated, the solicitation includes a detailed DARPA Standard Cost Proposal Spreadsheet for comprehensive cost tracking across multiple fiscal years and project phases. The research will be conducted at the contractor's facilities, with potential classified work environments. Proposers must demonstrate expertise in developing practical counter-AI technologies, with a focus on innovative approaches to identifying and mitigating vulnerabilities in AI-enabled battlefield systems. The program seeks to create an interoperable operational AI red teaming toolkit that can be used by the Department of Defense and broader U.S. Government agencies.
Notice text
2 versions
Update #2 · Latest ·
There is a growing desire to integrate rapidly advancing artificial intelligence (AI) technologies into Department of Defense (DoD) systems. AI may give battlefield advantage by helping improve the speed, quality, and accuracy of decision-making while enabling autonomy and assistive automation. Due to the statistical nature of machine learning, significant work has focused on ensuring the robustness of AI-enabled systems at inference time to natural degradations in performance caused by data distribution shifts (for example, from a highly dynamic deployment environment). However, as early as 2014, researchers demonstrated the ability to manipulate AI given adversary control of the input[1]. Additional work has further confirmed the theoretical risks of data poisoning[2], physically constrained adversarial patches for evasion[3], and model-stealing attacks[4]. These attacks are typically tested in simulated or physical environments with relatively pristine control compared to what might be expected on a battlefield. Today, there is still a limited ability to operationally assess deployed military AI-enabled systems for adversarial vulnerabilities and the “theoretical” adversarial AI attacks have not been practically demonstrated in operational settings. As a result, the operational security risks of AI-enabled battlefield systems remain largely unknown.
SABER aims to build an exemplar AI red team equipped with the necessary counter-AI techniques, tools, and technical competency to operationally assess AI-enabled battlefield systems. SABER seeks to establish a sustainable model for an operational AI red teaming process for the DoD. Our AI red team will target operationally assessing AI-enabled autonomous ground and aerial systems that could be deployed within the next 1-3 years. To assist the AI red team, the future solicitation will seek performers who can assist in surveying, evaluating, selecting, developing, and employing state-of-the-art physical (including m
Update #1 ·
There is a growing desire to integrate rapidly advancing artificial intelligence (AI) technologies into Department of Defense (DoD) systems. AI may give battlefield advantage by helping improve the speed, quality, and accuracy of decision-making while enabling autonomy and assistive automation. Due to the statistical nature of machine learning, significant work has focused on ensuring the robustness of AI-enabled systems at inference time to natural degradations in performance caused by data distribution shifts (for example, from a highly dynamic deployment environment). However, as early as 2014, researchers demonstrated the ability to manipulate AI given adversary control of the input[1]. Additional work has further confirmed the theoretical risks of data poisoning[2], physically constrained adversarial patches for evasion[3], and model-stealing attacks[4]. These attacks are typically tested in simulated or physical environments with relatively pristine control compared to what might be expected on a battlefield. Today, there is still a limited ability to operationally assess deployed military AI-enabled systems for adversarial vulnerabilities and the “theoretical” adversarial AI attacks have not been practically demonstrated in operational settings. As a result, the operational security risks of AI-enabled battlefield systems remain largely unknown.
SABER aims to build an exemplar AI red team equipped with the necessary counter-AI techniques, tools, and technical competency to operationally assess AI-enabled battlefield systems. SABER seeks to establish a sustainable model for an operational AI red teaming process for the DoD. Our AI red team will target operationally assessing AI-enabled autonomous ground and aerial systems that could be deployed within the next 1-3 years. To assist the AI red team, the future solicitation will seek performers who can assist in surveying, evaluating, selecting, developing, and employing state-of-the-art physical (including manufacturing/materials), adversarial AI (including digital), cyber, and electronic warfare techniques and tools, or other relevant vectors for operational assessment of AI-enabled battlefield system development and deployment pipelines[5]. Additionally, the forthcoming solicitation will seek a performer to serve as an integration lead, assisting in integrating the technologies into an interoperable, operational AI red teaming toolkit to enable future AI red teams for the DoD and broader U.S. Government.
Citations:
1 https://arxiv.org/abs/1412.6572
2 https://arxiv.org/abs/1804.00792
3 https://arxiv.org/pdf/2104.06728
4 https://arxiv.org/abs/2411.10023
5 https://sam.gov/opp/041e8bd594c1450dbec14a6ca580fbed/view
Attachments
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Notice history
| Notice | Type | Posted |
|---|---|---|
| Securing Artificial Intelligence for Battlefield Effective Robustness (SABER) | Award Notice | |
| Securing Artificial Intelligence for Battlefield Effective Robustness (SABER) | Award Notice | |
| Securing Artificial Intelligence for Battlefield Effective Robustness (SABER) | Solicitation |
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