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Friday, February 16, 2024

The Prime 10 Weblog Posts of 2023

Each January on the SEI Weblog, we current the ten most-visited posts of the earlier 12 months. This 12 months’s prime 10 highlights our work in quantum computing, software program modeling, giant language fashions, DevSecOps, and synthetic intelligence. The posts, which had been revealed between January 1, 2023, and December 31, 2023, are introduced beneath in reverse order based mostly on the variety of visits.

#10 Contextualizing Finish-Person Wants: 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 know-how and belief it? How can innovators design AI-enabled merchandise, companies, and capabilities which are efficiently adopted, quite than discarded as a result of the system fails to satisfy operational necessities, reminiscent of end-user confidence? AI’s promise is certain to perceptions of its trustworthiness.

To highlight just a few real-world situations, think about:

  • How does a software program engineer gauge the trustworthiness of automated code technology instruments to co-write practical, high quality code?
  • How does a physician gauge the trustworthiness of predictive healthcare purposes to co-diagnose affected person circumstances?
  • 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 potential to efficiently companion with the software program engineer, physician, or warfighter in these circumstances relies on whether or not these finish customers belief the AI system to companion successfully with them and ship the result promised. To construct acceptable ranges of belief, expectations have to be managed for what AI can realistically ship.

This weblog submit explores main analysis and classes discovered to advance dialogue of methods to measure the trustworthiness of AI so warfighters and finish customers on the whole can understand the promised outcomes.

Learn the submit in its entirety.

#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’s nonetheless appreciable uncertainty concerning the ZT transformation course of, nevertheless, 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 plenty of non-public sector organizations are following go well with. In response to those government orders, researchers at the SEI’s CERT Division hosted Zero Belief Business Days in August 2022 to allow trade stakeholders to share details about implementing ZT.

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

Learn the submit in its entirety.

#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 automobile. As you strategy a cease signal, as an alternative of stopping, the automobile hurries up and goes by the cease signal as a result of it interprets the cease signal as a pace restrict signal. How did this occur? Although the automobile’s machine studying (ML) system was skilled to acknowledge cease indicators, somebody added stickers to the cease signal, which fooled the automobile into pondering it was a 45-mph pace restrict signal. This straightforward act of placing stickers on a cease signal is one instance of an adversarial assault on ML methods.

On this SEI Weblog submit, I study how ML methods may 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 by which an ML mannequin may be influenced and present how this taxonomy can be utilized to tell fashions which are sturdy towards adversarial actions.

Learn the submit in its entirety.

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

by Jay Palat

“AI is not going to exchange 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 giant 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 giant our bodies of textual content, these fashions can predict the subsequent phrase to put in writing sentences and paragraphs of coherent content material. The priority surrounding these instruments is robust – from New York faculties banning the usage of ChatGPT to Stack Overflow and Reddit banning solutions and artwork generated from LLMs. Whereas many purposes are strictly restricted to writing textual content, just a few purposes discover the patterns to work on code, as nicely. The hype surrounding these purposes ranges from adoration (“I’ve rebuilt my workflow round these instruments”) to concern, 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 submit I’ll discover what new developments in AI and LLMs imply for software program growth.

Learn the submit in its entirety.

#6 Use Docker and NS-3 to Create Practical Community Simulations

by Alejandro Gomez

Typically, researchers and builders have to simulate numerous kinds of networks with software program that may in any other case be laborious to do with actual gadgets. For instance, some {hardware} may be laborious to get, costly to arrange, or past the talents of the crew to implement. When the underlying {hardware} just isn’t a priority however the important features 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 numerous sorts of networks and user-end gadgets, and its Python-to-C++ bindings make it accessible for a lot of builders.

In some instances, nevertheless, it isn’t enough to simulate a community. A simulator would possibly want to check how knowledge behaves in a simulated community (i.e., testing the integrity of Person Datagram Protocol (UDP) site 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 characteristic of digital community gadgets supplied by the Linux kernel that move ethernet frames to and from person house) into the working simulation.

This weblog submit presents a tutorial on how one can transmit reside knowledge by 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 advanced setups and make repeatable simulations in seconds.

Learn the submit in its entirety.

#5 5 Challenges to Implementing DevSecOps and 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. Lately, nevertheless, the dialogue has shifted to this system stage to align safety with enterprise aims. The best consequence of such a shift is one by which 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 extra concentrate on safety actions all through all phases of the software program growth lifecycle (SDLC), can assist organizations understand this ideally suited state. Nevertheless, the shift from project- to program-level pondering raises quite a few challenges. In our expertise, we’ve noticed 5 widespread challenges to implementing DevSecOps. This SEI Weblog submit articulates these challenges and supplies actions organizations can take to beat them.

Learn the submit in its entirety.

#4 Software 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 giant 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 identical time, plunging everybody right into a world the place it’s laborious to differentiate the superbly shaped, but generally pretend and incorrect, code generated by synthetic intelligence (AI) applications from verified and well-tested methods?

This weblog submit, which builds on concepts launched within the IEEE paper Software 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.

Learn the submit in its entirety.

#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 may be efficient in lots of conditions, Rust’s compiler may be very explicit 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 benefit from these vulnerabilities. On this submit, we are going to concentrate on customers of Rust applications, quite than Rust builders. We are going to discover some instruments for understanding vulnerabilities whether or not the unique supply code is obtainable or not. These instruments are essential for understanding malicious software program the place supply code is commonly unavailable, in addition to commenting on attainable instructions by which 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 that may impression future safety responses, together with through the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Heart (CERT/CC). This submit is the second in a collection exploring the Rust programming language. The first submit explored safety points with Rust.

Learn the submit in its entirety.

#2 Software program Modeling: What to Mannequin and Why

by John McGregor and Sholom G. Cohen

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

When initially envisioned, the necessities for a product could appear to characterize the precise product for the stakeholders. Throughout growth, nevertheless, 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 take a look at instances. Many questions come up, reminiscent of

  • How do I consider the failure of a take a look at?
  • How can I consider the completeness of a take a look at 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 needs to be the case for a set of fashions created throughout growth—are they nonetheless consultant of the applied product present process integration?

One of many objectives for sturdy design is to have an up-to-date single authoritative supply of fact by which discipline-specific views of the system are created utilizing the identical mannequin parts at every growth step. The only authoritative supply will typically be a group of requirement, specification, and design submodels inside 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 submit, we study the questions above and different questions that come up throughout growth and use the solutions to explain modeling and evaluation actions.

Learn the submit in its entirety.

#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 may have the ability to clear up sure issues which are classically intractable, which means a standard pc 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’s little or no dialogue, nevertheless, on how we are going to defend quantum computer systems sooner or later. If quantum computer systems are to develop into such beneficial belongings, it’s affordable to challenge that they may finally be the goal of malicious exercise.

I used to be not too long ago 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 Know-how 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 deal with looming cybersecurity issues. This submit 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.

Learn the submit in its entirety.

Wanting Forward in 2024

We publish a brand new submit 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.

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