ONHS Report: FA875025S7001

FOUNDATIONS OF TRUSTED SYSTEMS

Solicitation ID: FA875025S7001

Agency: AFRL/RITA

Type: Presolicitation

Deadline: 2028-12-04T14:00:00 ET

Eligibility: Maybe (Dual Approval Required)

Funding: Total funding is approximately $99.9M. Individual awards normally range from $100K to $5M, with the potential for larger awards up to the BAA ceiling.

Doc Type: BAA

Clearance: TS/SCI

Program Manager: Esteban Garcia

Last Updated: 2025-11-24 13:16

Analyzed File: 25-01 amend 2 update PM.docx

Eligibility Reasoning

Federally Funded Research and Development Centers (FFRDCs)... cannot propose to this BAA in any capacity unless they meet the following conditions: FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector; and FFRDCs must provide a letter on official letterhead from their sponsoring organization citing the specific authority establishing their eligibility... This information is required for FFRDCs proposing to be prime contractors or sub-awardees. ... FFRDC and Government entity eligibility will be determined on a case-by-case basis.

Note: This is an AI-generated summary...

Summary

The U.S. Air Force Research Laboratory (AFRL) has issued Broad Agency Announcement (BAA) FA8750-25-S-7001, 'Foundations of Trusted Systems', to solicit innovative research for establishing trusted hardware and software foundations for secure, resilient, and affordable Command, Control, Communications, Computers, Intelligence (C4I) and Cyber information processing systems. This is an open, two-step BAA effective until December 4, 2028, with a total estimated funding of $99.9 million. Initial submissions are limited to white papers, with formal proposals accepted by invitation only. Individual awards will typically range from $100K to $5M over a period of up to 36 months. Key technical areas of interest include: trusted and resilient computational systems, software assurance, cyber-physical systems (CPS), the application of AI in trusted systems, and digital engineering model-based analysis. An amendment updated the BAA Program Manager to 2d Lt. Jeffrey Benson.

Overall Technical Areas

Hardware Security & TrustSecure Computer ArchitectureSoftware Assurance (SwA)Formal Methods & VerificationModel-Based Systems EngineeringCyber-Physical Systems (CPS) SecurityAI / ML AssuranceAdversarial AI MitigationAI-Assisted Formal MethodsResilient & Self-Healing SoftwareRuntime Verification & ValidationCorrect-by-Construction DesignSoftware Model ExtractionSecure Software Development

Probable LBNL Areas

BSACSAEESAESAETAPSA

Focus Areas

Example Priorities:

Problems and challenges with current processor designs for trustworthiness and their solutions
Tools and methodologies for ensuring trustworthiness of integrated circuits (ICs) and systems targeting malicious inclusions such as hardware Trojans, side-channel timing attacks, etc.
New computer architectures for secure and reliable computing, including data integrity and code protection and verification.
Operating System level constructs, objects, functions, and distributions that must be provided to complement the hardware to enable a trustworthy computing base.
Hardware architecture techniques for self IC inspection, characterization, and testing/debug.

Specific Technical Skills:

Hardware SecurityComputer ArchitectureIntegrated Circuit (IC) DesignSide-Channel AnalysisHardware TrojansOperating System DesignPost-Fabrication VerificationSystem-on-Chip (SoC) SecurityFormal VerificationData Integrity

Potential PIs for this Area:

Name	Score	Organization
Sean Peisert	0.69	CSA \| SciData \| Data Science Applications
Angelos Ioannou	0.69	CSA \| AMCR \| Computer Science
Georgios Michelogiannakis	0.68	CSA \| AMCR \| Computer Science
Hasita Veluri	0.68	CSA \| AMCR \| Computer Science
Meriam Gay Bautista-Jurney	0.66	CSA \| AMCR \| Computer Science
Tiffany Connors	0.66	CSA \| NERSC \| Data Center Department
Samuel Williams	0.66	CSA \| AMCR \| Computer Science
Khaled Ibrahim	0.66	CSA \| AMCR \| Computer Science
Alex Newkirk	0.65	ETA \| Building Technology & Urban Systems \| Bldg & Industrial App Dpt
Edward Bethel	0.65	CSA \| SciData \| Data Science Research

Example Priorities:

Achieve SwA across the application space through a holistic approach, agilely addressing all layers of the supply chain and enterprise including governance, partnerships, process, standards, and education.
Formal methods tools, including connecting proofs of the models to actual code and auditable proof production for tools and static analysis algorithms
Provably correct code generation and repair
Novel architectural approaches, strategic 'roots of trust,' hardening and resiliency techniques such as artificial diversity, containment areas for execution of untrusted software
Autonomous resilience, self-healing software systems, and pliable computing.
Extraction of models from source code

Specific Technical Skills:

Software Assurance (SwA)Formal MethodsModel-Based Systems Engineering (MBSE)DevSecOpsStatic Code AnalysisVerification & Validation (V&V)Resilient Systems DesignSelf-Healing SoftwareDigital EngineeringBinary Analysis

Potential PIs for this Area:

Name	Score	Organization
Gregory Lemieux	0.69	EESA \| CESD \| Earth System Sciences
Karlo Berket	0.68	CSA \| SciData \| Data Science Applications
Dan Bonachea	0.68	CSA \| AMCR \| Computer Science
Vinay Sawal	0.67	CSA \| NERSC \| Data Center Department
Sean Peisert	0.67	CSA \| SciData \| Data Science Applications
Nestor Demeure	0.67	CSA \| NERSC \| HPC Technology Department
Khaled Ibrahim	0.67	CSA \| AMCR \| Computer Science
Rohan Adwankar	0.67	BSA \| Biological Systems & Engineering \| Process Engr & Analytics
Oluwamayowa Amusat	0.67	CSA \| SciData \| Data Science Applications
Stefan Lasiewski	0.67	CSA \| NERSC \| HPC Technology Department

Example Priorities:

Using captured intent to drastically reduce the human-in-the-loop effort needed to adapt software to new requirements, platforms, and resources
Continual assurance technology for learning-enabled components.
Establishing robust mechanisms to monitor, validate, and assess machine learning models
Detecting and mitigating security vulnerabilities, including drift, bias, and adversarial attacks in AI systems.
Utilizing AI to streamline the adoption, automation, and scalability of formal methods and verification techniques in software development, enhancing usability, reducing expertise barriers, and facilitating more effective human-machine collaboration.

Specific Technical Skills:

Machine LearningAI SafetyAdversarial AIExplainable AI (XAI)Continual AssuranceHuman-Machine TeamingCorrect-by-Construction DesignAI/ML Model ValidationAutomated Decision-MakingNatural Language Processing

Potential PIs for this Area:

Name	Score	Organization
Gazi Mahmud	0.70	BSA \| Environmental Genomics & Systems Biology \| Molecular Ecosystems Bio
Neel Rajeshbhai Vora	0.68	PSA \| ATAP \| BACI
Oluwamayowa Amusat	0.67	CSA \| SciData \| Data Science Applications
Xiaoya Chong	0.67	ESA \| ALS \| PS Computing staff
Marcos Lopez de Prado	0.67	CSA \| SciData \| Data Science Research
Shi Wang	0.67	EESA \| Earth & Environmental Sciences \| Operations Dept
Rohan Adwankar	0.67	BSA \| Biological Systems & Engineering \| Process Engr & Analytics
Edward Bethel	0.67	CSA \| SciData \| Data Science Research
Han Li	0.67	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Sufi Kaur	0.66	CSA \| SciData \| Data Science Applications

Example Priorities:

develop the tools, methodologies, and foundational science providing required scalability and interoperability to make integration and subsequent analysis of computational and physical components and the networks connecting them practical.
Specification, modeling, and analysis such as heterogeneous models of computation and hybrid discrete and continuous systems;
Modularity, composability, predictability, and synthesis addressing the issues of scalability and the integration of legacy systems;
Verification and validation techniques (assurance, simulation, formal methods, correct-and-secure-by-construction, etc.) applicable to these models.

Specific Technical Skills:

Cyber-Physical Systems (CPS)Hybrid Systems ModelingControl Systems TheorySystem IntegrationFormal Verification (CPS)Network SecurityEmbedded Systems SecuritySimulation & ModelingSystem ComposabilityPredictable Systems

Potential PIs for this Area:

Name	Score	Organization
Angelos Ioannou	0.69	CSA \| AMCR \| Computer Science
Alexandre Moreira da Silva	0.69	ETA \| ESDR \| Energy Storage & Dist Dpt
Anand Krishnan Prakash	0.68	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Michael Wetter	0.68	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
David Blum	0.68	ETA \| Building Technology & Urban Systems \| Bldg Technologies Dept
Sean Peisert	0.68	CSA \| SciData \| Data Science Applications
Nestor Demeure	0.68	CSA \| NERSC \| HPC Technology Department
Tiffany Connors	0.68	CSA \| NERSC \| Data Center Department
Douglas Black	0.67	ETA \| ESDR \| Energy Storage & Dist Dpt
Dan Bonachea	0.67	CSA \| AMCR \| Computer Science

Potential LBNL PIs (Overall)

Note on PI Matching: These suggestions are generated through an AI-driven semantic analysis of LBNL staff profiles.

Name	Score	Organization
Jason Lowe-Power	0.77	CSA \| SciData \| Data Science Applications · Secure Computer Architecture
Angelos Ioannou	0.75	CSA \| AMCR \| Computer Science · Hardware Security & Trust
Alexandre Bayen	0.74	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Cyber-Physical Systems (CPS) Security
Koushik Sen	0.74	CSA \| AMCR \| Computer Science · Runtime Verification & Validation
Daniel Arnold	0.73	ETA \| ESDR \| Energy Storage & Dist Dpt · Adversarial AI Mitigation
Steven Hofmeyr	0.73	CSA \| AMCR \| Computer Science · Secure Software Development
Gregory Lemieux	0.72	EESA \| CESD \| Earth System Sciences · Resilient & Self-Healing Software
Anna Giannakou	0.72	CSA \| SciData \| Data Science Applications · AI / ML Assurance
Zachary Needell	0.72	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Model-Based Systems Engineering
Eric Chagnon	0.71	CSA \| AMCR \| Mathematics · AI-Assisted Formal Methods

Fundamental Research Exemption (FRE)

It is DoD policy that the publication of products of fundamental research will remain unrestricted to the maximum extent possible. National Security Decision Directive (NSDD) 189 defines fundamental research as follows: ‘Fundamental research’ means basic and applied research in science and engineering, the results of which ordinarily are published and shared broadly within the scientific community, as distinguished from proprietary research and from industrial development, design, production, and product utilization, the results of which ordinarily are restricted for proprietary or national security reasons. As of the date of publication of this BAA, the Government cannot identify whether work proposed under this BAA may be considered fundamental research and may award both fundamental and non-fundamental research.