ONHS Report: FA489027RFI0004

RFI CNET Reverse Engineering and AI

Solicitation ID: FA489027RFI0004

Agency: JWAC/CNET LAB

Type: Sources Sought

Deadline: 2026-11-15T12:00:00 ET

Eligibility: Maybe (Info Requested)

Funding: N/A

Doc Type: RFI

Clearance: TS/SCI

Program Manager: N/A

Last Updated: 2026-11-03 17:03

Analyzed File: (U) CNET LAB Reverse Engineering and Artificial Intelligence (REAI) FY28.pdf

Eligibility Reasoning

Does your company have analysts at the TS//SCI level?

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

Summary

The Joint Warfare Analysis Center (JWAC) is issuing a Request for Information (RFI) to identify contractors capable of providing specialized engineering support for its CNET Lab in Dahlgren, VA. The effort requires on-site personnel with TS//SCI clearance to support equipment maintenance, troubleshooting, and proof-of-concept tactic development within telecommunications and computer network environments. The core technical requirements are expertise in two areas: 1) Software Reverse Engineering, focusing on analyzing software, binaries, and firmware from various architectures (e.g., x86, ARM, MIPS) to support threat network and TTP development. 2) Artificial Intelligence, requiring deep expertise in the full lifecycle of foundation model development, including pre-training on massive datasets (PCAP, simulation outputs, RE artifacts), distributed training on GPU clusters, post-training alignment, and optimized production deployment for tasks related to cybersecurity and network analysis. (Source: (U) CNET LAB Reverse Engineering and Artificial Intelligence (REAI) FY28.pdf.)

Overall Technical Areas

Software Reverse EngineeringArtificial IntelligenceFoundation Model DevelopmentDistributed AI/ML TrainingLarge-Scale Data PipelinesHardware-Software Co-designNeurosymbolic AI IntegrationAdversarial Machine LearningAI/ML Inference OptimizationNetwork Modeling and SimulationNetwork Data AnalysisCyber Threat AnalysisMulti-Architecture Reverse EngineeringCryptography and Obfuscation AnalysisEmbedded Systems Security

Probable LBNL Areas

BSACSAEESAETA

Focus Areas

Example Priorities:

The contractor will provide software reverse engineers on site in Dahlgren, VA with expertise of software and/or telecommunications/computer components that deal with national security interests.
JWAC may provide the contractor source code, binaries or hardware for firmware extraction.
Reverse engineering will be used to support threat network and Tactics, Techniques and Procedure (TTP) development.
How have you dealt with cryptography & obfuscation analysis? Specifically address areas related to: Algorithm Identification, White-Box Crypto, De-obfuscation.
Have you worked in any/all of these areas? Malware Analysis / Threat Intel, Embedded / IOT / Firmware, Mobile (Android / iOS).

Specific Technical Skills:

Software Reverse EngineeringFirmware ExtractionAssembly Languages (x86, ARM, MIPS)Operating System InternalsCryptography AnalysisDe-obfuscation TechniquesStatic & Dynamic AnalysisBinary InstrumentationMalware AnalysisEmbedded & IoT Security

Potential PIs for this Area:

Name	Score	Organization
Koushik Sen	0.75	CSA \| AMCR \| Computer Science · Static & Dynamic Analysis
Alexandre Bayen	0.75	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Cryptography Analysis
Gregory Lemieux	0.72	EESA \| CESD \| Earth System Sciences · Software Reverse Engineering
Angelos Ioannou	0.72	CSA \| AMCR \| Computer Science · Assembly Languages (x86, ARM, MIPS)
Tiffany Connors	0.72	CSA \| NERSC \| Data Center Department · Malware Analysis
Steven Hofmeyr	0.71	CSA \| AMCR \| Computer Science · Operating System Internals
Jason Lowe-Power	0.71	CSA \| SciData \| Data Science Applications · Assembly Languages (x86, ARM, MIPS)
Zachary Needell	0.71	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Static & Dynamic Analysis
Seth Carbon	0.71	BSA \| Env. Genomics & Sys. Biology \| BioSystems Data Science · Firmware Extraction
Katherine Rasmussen	0.70	CSA \| AMCR \| Computer Science · Binary Instrumentation

Example Priorities:

The contract will need to possess deep expertise across the full lifecycle of foundation model development, spanning pre-training at trillion-token scale through post-training alignment and production deployment.
Architect and maintain high-throughput ingestion, deduplication, filtering, and streaming pipelines that transform massive internal datasets-including packet capture (PCAP) archives, digital twin simulation outputs, and software reverse engineering artifacts-into high-quality training data.
Apply expertise in the rapidly evolving landscape of agentic artificial intelligence (Al), test-time compute scaling to allocate more inference compute for harder problems, and neurosymbolic integration to combine neural networks with symbolic reasoning engines for verifiable logic.
As needed, utilize machine learning (ML) security, adversarial robustness, and model reverse engineering-capabilities that intersect directly with the supported team's mission in cyber security, software reverse engineering, and PCAP analytics.

Specific Technical Skills:

Foundation Model DevelopmentDistributed GPU TrainingLarge-Scale Data Pipelines (Spark, Dask)PCAP AnalyticsAgentic AINeurosymbolic IntegrationMachine Learning SecurityAdversarial RobustnessInference Optimization (vLLM, TensorRT-LLM)Post-Training Alignment (RLHF, DPO)

Potential PIs for this Area:

Name	Score	Organization
Cees De Laat	0.76	CSA \| ESNET \| Tech Adv and Engagement · Large-Scale Data Pipelines (Spark, Dask)
Suren Byna	0.75	CSA \| SciData \| Data Science Research · Inference Optimization (vLLM, TensorRT-LLM)
Julien Esseiva	0.74	CSA \| SciData \| Data & Computational Science · Distributed GPU Training
Tiffany Connors	0.74	CSA \| NERSC \| Data Center Department · Machine Learning Security
Houjun Tang	0.73	CSA \| SciData \| Data Science Applications · Agentic AI
Eric Chagnon	0.73	CSA \| AMCR \| Mathematics · Foundation Model Development
Daniel Arnold	0.72	ETA \| Energy Technologies and Systems \| Energy Storage & Dist Dpt · Adversarial Robustness
Arpit Gupta	0.72	CSA \| ESNET \| Tech Adv and Engagement · PCAP Analytics
Christoph Kirst	0.71	CSA \| SciData \| Data & Computational Science · Neurosymbolic Integration
Matthew Henderson	0.71	CSA \| SciData \| Data Science Applications · Post-Training Alignment (RLHF, DPO)

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
Cees De Laat	0.76	CSA \| ESNET \| Tech Adv and Engagement · Large-Scale Data Pipelines
Scott Beamer	0.75	CSA \| AMCR \| Computer Science · Hardware-Software Co-design
Anna Giannakou	0.74	CSA \| SciData \| Data Science Applications · Distributed AI/ML Training
Suren Byna	0.74	CSA \| SciData \| Data Science Research · AI/ML Inference Optimization
Evangelos Chaniotakis	0.73	CSA \| ESNET \| Systems and Software · Network Modeling and Simulation
Eric Chagnon	0.72	CSA \| AMCR \| Mathematics · Foundation Model Development
Gregory Lemieux	0.72	EESA \| CESD \| Earth System Sciences · Software Reverse Engineering
Alexandre Bayen	0.72	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Cyber Threat Analysis
Christoph Kirst	0.72	CSA \| SciData \| Data & Computational Science · Neurosymbolic AI Integration
Daniel Arnold	0.71	ETA \| Energy Technologies and Systems \| Energy Storage & Dist Dpt · Adversarial Machine Learning

Fundamental Research Exemption (FRE)

Not Mentioned