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Securing Artificial Intelligence for Battlefield Effective Robustness (SABER)

USARFP notice for Securing Artificial Intelligence for Battlefield Effective Robustness (SABER). The reference ID of the tender is 116170459 and it is closing on 06 May 2025.

Tender Details

  • Country: USA
  • Summary: Securing Artificial Intelligence for Battlefield Effective Robustness (SABER)
  • UST Ref No: 116170459
  • Deadline: 06 May 2025
  • Financier: Self Financed
  • Purchaser Ownership: Government
  • Tender Value: Refer Document
  • Notice Type: Tender
  • Document Ref. No.: HR001125S0009
  • Purchaser's Detail:
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  • Description:
  • Description
    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 β€œtheoretical” 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 ...
  • Documents:

 Tender Notice

HR001125S0009.pdf

Baseline_Model-_OT_P-_Fixed_Support_Nontradition.docx

Baseline_Model-_OT_R-_Fixed_Support_Company.docx

Baseline_Model-_Contract_Addendum_Circumstance-Dri.docx

Baseline_Model-_Contract_Large_Business_Dec_2024.pdf

Baseline_Model-_Contract_Small_Business_Dec_2024.pdf

P4-_DARPA_Standard_Cost_Proposal_Spreadsheet.xlsx

P3_Proposal_Instructions_and_Volume_II_Template_C.docx

P2_Proposal_Instructions_and_Volume_I_Template_Te.docx

P1_Proposal_Summary_Slide_Instructions_and_Templat.pptx

A-2_Abstract_Instructions_and_Submission_Template.docx

A1_Abstract_Summary_Slide_Instructions_and_Templat.pptx

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Securing Artificial Intelligence for Battlefield Effective Robustness (SABER) - USA Tender

The DEPT OF DEFENSE | DEFENSE ADVANCED RESEARCH PROJECTS AGENCY (DARPA), a Government sector organization in USA, has announced a new tender for Securing Artificial Intelligence for Battlefield Effective Robustness (SABER). This tender is published on USARFP under UST Ref No: 116170459 and is categorized as a Tender. Interested and eligible suppliers are invited to participate by reviewing the tender documents and submitting their bids before the deadline on 2025-05-06.

The estimated tender value is Refer Document, and full details, including technical specifications and submission requirements, are provided in the official tender documents. Ensure all submissions meet the criteria outlined to be considered for evaluation.

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