Each January on the SEI Weblog, we current the ten most-visited posts of the earlier yr. This yr’s high 10 highlights our work in quantum computing, software program modeling, massive language fashions, DevSecOps, and synthetic intelligence. The posts, which have been printed between January 1, 2023, and December 31, 2023, are offered beneath in reverse order based mostly on the variety of visits.

#10 Contextualizing Finish-Consumer Wants: Find out how to Measure the Trustworthiness of an AI System

by Carrie Gardner, Katherine-Marie Robinson, Carol J. Smith, and Alexandrea Steiner

As potential purposes of synthetic intelligence (AI) proceed to broaden, the query stays: will customers need the expertise and belief it? How can innovators design AI-enabled merchandise, companies, and capabilities which can be efficiently adopted, somewhat than discarded as a result of the system fails to satisfy operational necessities, equivalent to end-user confidence? AI’s promise is certain to perceptions of its trustworthiness.

To highlight a couple of real-world situations, contemplate:

• How does a software program engineer gauge the trustworthiness of automated code technology instruments to co-write purposeful, high quality code?
• How does a health care provider gauge the trustworthiness of predictive healthcare purposes to co-diagnose affected person situations?
• How does a warfighter gauge the trustworthiness of computer-vision enabled risk intelligence to co-detect adversaries?

What occurs when customers don’t belief these methods? AI’s means to efficiently associate with the software program engineer, physician, or warfighter in these circumstances will depend on whether or not these finish customers belief the AI system to associate successfully with them and ship the result promised. To construct acceptable ranges of belief, expectations should be managed for what AI can realistically ship.

This weblog publish explores main analysis and classes discovered to advance dialogue of how you can measure the trustworthiness of AI so warfighters and finish customers basically can understand the promised outcomes.

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#9 5 Finest Practices from Business for Implementing a Zero Belief Structure

by Matthew Nicolai, Nathaniel Richmond, and Timothy Morrow

Zero belief (ZT) structure (ZTA) has the potential to enhance an enterprise’s safety posture. There may be nonetheless appreciable uncertainty concerning the ZT transformation course of, nonetheless, in addition to how ZTA will in the end seem in observe. Current government orders M-22-009 and M-21-31 have accelerated the timeline for zero belief adoption within the federal sector, and lots of non-public sector organizations are following swimsuit. In response to those government orders, researchers at the SEI’s CERT Division hosted Zero Belief Business Days in August 2022 to allow business stakeholders to share details about implementing ZT.

On this weblog publish, which we tailored from a white paper, we element 5 ZT finest practices recognized through the two-day occasion, talk about why they’re vital, and supply SEI commentary and evaluation on methods to empower your group’s ZT transformation.

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#8 The Problem of Adversarial Machine Studying

by Matt Churilla, Nathan M. VanHoudnos, and Robert W. Beveridge

Think about using to work in your self-driving automotive. As you strategy a cease signal, as a substitute of stopping, the automotive hastens and goes via the cease signal as a result of it interprets the cease signal as a pace restrict signal. How did this occur? Regardless that the automotive’s machine studying (ML) system was skilled to acknowledge cease indicators, somebody added stickers to the cease signal, which fooled the automotive into pondering it was a 45-mph pace restrict signal. This easy act of placing stickers on a cease signal is one instance of an adversarial assault on ML methods.

On this SEI Weblog publish, I study how ML methods might be subverted and, on this context, clarify the idea of adversarial machine studying. I additionally study the motivations of adversaries and what researchers are doing to mitigate their assaults. Lastly, I introduce a primary taxonomy delineating the methods wherein an ML mannequin might be influenced and present how this taxonomy can be utilized to tell fashions which can be strong towards adversarial actions.

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#7 Play it Once more Sam! or How I Discovered to Love Giant Language Fashions

by Jay Palat

“AI won’t substitute you. An individual utilizing AI will.”

-Santiago @svpino

In our work as advisors in software program and AI engineering, we are sometimes requested concerning the efficacy of AI code assistant instruments like Copilot, GhostWriter, or Tabnine based mostly on massive language mannequin (LLM). Current innovation within the constructing and curation of LLMs demonstrates highly effective instruments for the manipulation of textual content. By discovering patterns in massive our bodies of textual content, these fashions can predict the subsequent phrase to write down sentences and paragraphs of coherent content material. The priority surrounding these instruments is robust – from New York colleges banning using ChatGPT to Stack Overflow and Reddit banning solutions and artwork generated from LLMs. Whereas many purposes are strictly restricted to writing textual content, a couple of purposes discover the patterns to work on code, as effectively. The hype surrounding these purposes ranges from adoration (“I’ve rebuilt my workflow round these instruments”) to worry, uncertainty, and doubt (“LLMs are going to take my job”). Within the Communications of the ACM, Matt Welsh goes as far as to declare we’ve reached “The Finish of Programming.” Whereas built-in growth environments have had code technology and automation instruments for years, on this publish I’ll discover what new developments in AI and LLMs imply for software program growth.

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#6 Find out how to Use Docker and NS-3 to Create Life like Community Simulations

by Alejandro Gomez

Typically, researchers and builders must simulate varied kinds of networks with software program that may in any other case be onerous to do with actual gadgets. For instance, some {hardware} might be onerous to get, costly to arrange, or past the talents of the workforce to implement. When the underlying {hardware} is just not a priority however the important capabilities that it does is, software program generally is a viable different.

NS-3 is a mature, open-source networking simulation library with contributions from the Lawrence Livermore Nationwide Laboratory , Google Summer time of Code, and others. It has a excessive diploma of functionality to simulate varied sorts of networks and user-end gadgets, and its Python-to-C++ bindings make it accessible for a lot of builders.

In some instances, nonetheless, it is not adequate to simulate a community. A simulator would possibly want to check how knowledge behaves in a simulated community (i.e., testing the integrity of Consumer Datagram Protocol (UDP) visitors in a Wi-Fi community, how 5G knowledge propagates throughout cell towers and person gadgets, and many others. NS-3 permits such sorts of simulations by piping knowledge from faucet interfaces (a function of digital community gadgets offered by the Linux kernel that go ethernet frames to and from person house) into the operating simulation.

This weblog publish presents a tutorial on how one can transmit dwell knowledge via an NS-3-simulated community with the added benefit of getting the data-producing/data-receiving nodes be Docker containers. Lastly, we use Docker Compose to automate complicated setups and make repeatable simulations in seconds.

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#5 5 Challenges to Implementing DevSecOps and Find out how to Overcome Them

by Joe Yankel and Hasan Yasar

Traditionally, software program safety has been addressed on the challenge stage, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Not too long ago, nonetheless, the dialogue has shifted to this system stage to align safety with enterprise targets. The best final result of such a shift is one wherein software program growth groups act in alignment with enterprise objectives, organizational threat, and answer architectures, and these groups perceive that safety practices are integral to enterprise success. DevSecOps, which builds on DevOps rules and locations further deal with safety actions all through all phases of the software program growth lifecycle (SDLC), will help organizations understand this ultimate state. Nonetheless, the shift from project- to program-level pondering raises quite a few challenges. In our expertise, we’ve noticed 5 frequent challenges to implementing DevSecOps. This SEI Weblog publish articulates these challenges and supplies actions organizations can take to beat them.

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#4 Utility of Giant Language Fashions (LLMs) in Software program Engineering: Overblown Hype or Disruptive Change?

by Ipek Ozkaya, Anita Carleton, John E. Robert, and Douglas Schmidt (Vanderbilt College)

Has the day lastly arrived when massive language fashions (LLMs) flip us all into higher software program engineers? Or are LLMs creating extra hype than performance for software program growth, and, on the similar time, plunging everybody right into a world the place it’s onerous to differentiate the superbly shaped, but typically pretend and incorrect, code generated by synthetic intelligence (AI) applications from verified and well-tested methods?

This weblog publish, which builds on concepts launched within the IEEE paper Utility of Giant Language Fashions to Software program Engineering Duties: Alternatives, Dangers, and Implications by Ipek Ozkaya, focuses on alternatives and cautions for LLMs in software program growth, the implications of incorporating LLMs into software-reliant methods, and the areas the place extra analysis and improvements are wanted to advance their use in software program engineering.

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#3 Rust Vulnerability Evaluation and Maturity Challenges

by Garret Wassermann and David Svoboda

Whereas the reminiscence security and security measures of the Rust programming language might be efficient in lots of conditions, Rust’s compiler could be very specific on what constitutes good software program design practices. Every time design assumptions disagree with real-world knowledge and assumptions, there may be the potential for safety vulnerabilities–and malicious software program that may reap the benefits of these vulnerabilities. On this publish, we’ll deal with customers of Rust applications, somewhat than Rust builders. We are going to discover some instruments for understanding vulnerabilities whether or not the unique supply code is out there or not. These instruments are vital for understanding malicious software program the place supply code is commonly unavailable, in addition to commenting on potential instructions wherein instruments and automatic code evaluation can enhance. We additionally touch upon the maturity of the Rust software program ecosystem as a complete and the way which may affect future safety responses, together with by way of the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Middle (CERT/CC). This publish is the second in a sequence exploring the Rust programming language. The first publish explored safety points with Rust.

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#2 Software program Modeling: What to Mannequin and Why

by John McGregor and Sholom G. Cohen

Mannequin-based methods engineering (MBSE) environments are supposed to assist engineering actions of all stakeholders throughout the envisioning, growing, and sustaining phases of software-intensive merchandise. Fashions, the machine-manipulable representations and the merchandise of an MBSE setting, assist efforts such because the automation of standardized evaluation strategies by all stakeholders and the upkeep of a single authoritative supply of reality about product data. The mannequin faithfully represents the ultimate product in these attributes of curiosity to varied stakeholders. The result’s an general discount of growth dangers.

When initially envisioned, the necessities for a product could appear to signify the proper product for the stakeholders. Throughout growth, nonetheless, the as-designed product involves mirror an understanding of what’s actually wanted that’s superior to the unique set of necessities. When it’s time to combine elements, throughout an early incremental integration exercise or a full product integration, the unique set of necessities is now not represented and is now not a legitimate supply of check instances. Many questions come up, equivalent to

• How do I consider the failure of a check?
• How can I consider the completeness of a check set?
• How do I observe failures and the fixes utilized to them?
• How do I do know that fixes utilized don’t break one thing else?

Such is the case with necessities, and far the identical must be the case for a set of fashions created throughout growth—are they nonetheless consultant of the carried out product present process integration?

One of many objectives for strong design is to have an up-to-date single authoritative supply of reality wherein discipline-specific views of the system are created utilizing the identical mannequin components at every growth step. The only authoritative supply will typically be a group of requirement, specification, and design submodels throughout the product mannequin. The ensuing mannequin can be utilized as a legitimate supply of full and proper verification and validation (V&V) actions. On this publish, we study the questions above and different questions that come up throughout growth and use the solutions to explain modeling and evaluation actions.

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#1 Cybersecurity of Quantum Computing: A New Frontier

by Tom Scanlon

Analysis and growth of quantum computer systems continues to develop at a fast tempo. The U.S. authorities alone spent greater than $800 million on quantum data science (QIS) analysis in 2022. The promise of quantum computer systems is substantial – they’ll be capable to clear up sure issues which can be classically intractable, which means a traditional laptop can not full the calculations inside human-usable timescales. Given this computational energy, there may be rising dialogue surrounding the cyber threats quantum computer systems might pose sooner or later. As an illustration, Alejandro Mayorkas, secretary of the Division of Homeland Safety, has recognized the transition to post-quantum encryption as a precedence to make sure cyber resilience. There may be little or no dialogue, nonetheless, on how we’ll defend quantum computer systems sooner or later. If quantum computer systems are to turn into such beneficial belongings, it’s cheap to challenge that they’ll ultimately be the goal of malicious exercise.

I used to be lately invited to be a participant within the Workshop on Cybersecurity of Quantum Computing, co-sponsored by the Nationwide Science Basis (NSF) and the White Home Workplace of Science and Expertise Coverage, the place we examined the rising subject of cybersecurity for quantum computing. Whereas quantum computer systems are nonetheless nascent in some ways, it’s by no means too early to handle looming cybersecurity considerations. This publish will discover points associated to creating the self-discipline of cyber safety of quantum computing and description six areas of future analysis within the subject of quantum cybersecurity.

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Trying Forward in 2024

We publish a brand new publish on the SEI Weblog each Monday morning. Within the coming months, search for posts highlighting the SEI’s work in synthetic intelligence, cybersecurity, and edge computing.