Each January on the SEI Weblog, we current the ten most-visited posts of the earlier yr. This yr’s prime 10 highlights our work in quantum computing, software program modeling, massive language fashions, DevSecOps, and synthetic intelligence. The posts, which had been revealed between January 1, 2023, and December 31, 2023, are offered under in reverse order based mostly on the variety of visits.
#10 Contextualizing Finish-Person Wants: The right way 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 develop, the query stays: will customers need the know-how and belief it? How can innovators design AI-enabled merchandise, providers, and capabilities which can be efficiently adopted, reasonably than discarded as a result of the system fails to satisfy operational necessities, akin to end-user confidence? AI’s promise is sure to perceptions of its trustworthiness.
To highlight just a few real-world situations, take into account:
- How does a software program engineer gauge the trustworthiness of automated code era instruments to co-write practical, 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 menace intelligence to co-detect adversaries?
What occurs when customers don’t belief these programs? AI’s skill to efficiently associate with the software program engineer, physician, or warfighter in these circumstances is dependent upon 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 have to be managed for what AI can realistically ship.
This weblog submit explores main analysis and classes realized to advance dialogue of the way to measure the trustworthiness of AI so warfighters and finish customers normally can understand the promised outcomes.
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#9 5 Greatest 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, nonetheless, in addition to how ZTA will in the end seem in apply. Latest government orders M-22-009 and M-21-31 have accelerated the timeline for zero belief adoption within the federal sector, and plenty of personal 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 submit, 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 method a cease signal, as a substitute of stopping, the automotive hurries up and goes by the cease signal as a result of it interprets the cease signal as a velocity restrict signal. How did this occur? Regardless that the automotive’s machine studying (ML) system was educated to acknowledge cease indicators, somebody added stickers to the cease signal, which fooled the automotive into pondering it was a 45-mph velocity restrict signal. This straightforward act of placing stickers on a cease signal is one instance of an adversarial assault on ML programs.
On this SEI Weblog submit, I study how ML programs 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 Realized to Love Giant Language Fashions
by Jay Palat
“AI is not going to change 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). Latest 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 powerful – 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, 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 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 era and automation instruments for years, on this submit I’ll discover what new developments in AI and LLMs imply for software program growth.
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#6 The right way to Use Docker and NS-3 to Create Lifelike Community Simulations
by Alejandro Gomez
Generally, researchers and builders must simulate varied kinds of networks with software program that may in any other case be arduous to do with actual units. For instance, some {hardware} might be arduous to get, costly to arrange, or past the abilities of the crew to implement. When the underlying {hardware} will not be a priority however the important capabilities that it does is, software program could be a viable various.
NS-3 is a mature, open-source networking simulation library with contributions from the Lawrence Livermore Nationwide Laboratory , Google Summer season of Code, and others. It has a excessive diploma of functionality to simulate varied sorts of networks and user-end units, and its Python-to-C++ bindings make it accessible for a lot of builders.
In some instances, nonetheless, it is not 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 units, and so on. NS-3 permits such sorts of simulations by piping knowledge from faucet interfaces (a function of digital community units offered 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.
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#5 5 Challenges to Implementing DevSecOps and The right way to Overcome Them
by Joe Yankel and Hasan Yasar
Traditionally, software program safety has been addressed on the undertaking degree, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Just lately, nonetheless, the dialogue has shifted to this system degree to align safety with enterprise aims. The best consequence 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 ideas and locations further give attention to safety actions all through all phases of the software program growth lifecycle (SDLC), may also help 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.
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#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 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 identical time, plunging everybody right into a world the place it’s arduous to tell apart the superbly shaped, but typically faux and incorrect, code generated by synthetic intelligence (AI) packages from verified and well-tested programs?
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 programs, 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 safety features 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 benefit from these vulnerabilities. On this submit, we’ll give attention to customers of Rust packages, reasonably than Rust builders. We are going to discover some instruments for understanding vulnerabilities whether or not the unique supply code is accessible or not. These instruments are essential 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 an entire and the way that may affect future safety responses, together with through the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Middle (CERT/CC). This submit is the second in a collection exploring the Rust programming language. The first submit 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 programs 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 surroundings, 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 info. 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 symbolize the correct product for the stakeholders. Throughout growth, nonetheless, the as-designed product involves replicate an understanding of what’s actually wanted that’s superior to the unique set of necessities. When it’s time to combine parts, throughout an early incremental integration exercise or a full product integration, the unique set of necessities is not represented and is not a legitimate supply of check instances. Many questions come up, akin 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 ought to 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 one 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.
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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 speedy tempo. The U.S. authorities alone spent greater than $800 million on quantum info science (QIS) analysis in 2022. The promise of quantum computer systems is substantial – they are going to have the ability 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 example, 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, nonetheless, on how we’ll shield quantum computer systems sooner or later. If quantum computer systems are to grow to be such worthwhile property, it’s cheap to undertaking that they are going to ultimately be the goal of malicious exercise.
I used to be just 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 area 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 area of quantum cybersecurity.
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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.