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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, giant language fashions, DevSecOps, and synthetic intelligence. The posts, which have 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-Consumer Wants: The way to Measure the Trustworthiness of an AI System
by Carrie Gardner, Katherine-Marie Robinson, Carol J. Smith, and Alexandrea Steiner
As potential functions 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 might be efficiently adopted, quite than discarded as a result of the system fails to fulfill operational necessities, comparable 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 useful, high quality code?
- How does a physician gauge the trustworthiness of predictive healthcare functions 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 means to efficiently companion 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 companion successfully with them and ship the end result promised. To construct applicable ranges of belief, expectations have to be managed for what AI can realistically ship.
This weblog publish 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 notice the promised outcomes.
Learn the publish in its entirety.
#9 5 Greatest Practices from Trade 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 in regards to the ZT transformation course of, nevertheless, in addition to how ZTA will in the end seem in observe. Current govt 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 govt orders, researchers at the SEI’s CERT Division hosted Zero Belief Trade Days in August 2022 to allow trade 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 important, and supply SEI commentary and evaluation on methods to empower your group’s ZT transformation.
Learn the publish in its entirety.
#8 The Problem of Adversarial Machine Studying
by Matt Churilla, Nathan M. VanHoudnos, and Robert W. Beveridge
Think about driving to work in your self-driving automotive. As you method a cease signal, as an alternative of stopping, the automotive hastens and goes by the cease signal as a result of it interprets the cease signal as a velocity restrict signal. How did this occur? Though 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 considering 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 methods.
On this SEI Weblog publish, I study how ML methods will 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 during which an ML mannequin will be influenced and present how this taxonomy can be utilized to tell fashions which might be strong towards adversarial actions.
Learn the publish in its entirety.
#7 Play it Once more Sam! or How I Discovered to Love Massive Language Fashions
by Jay Palat
“AI won’t exchange you. An individual utilizing AI will.”
-Santiago @svpino
In our work as advisors in software program and AI engineering, we are sometimes requested in regards to 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 following phrase to write down sentences and paragraphs of coherent content material. The priority surrounding these instruments is robust – from New York colleges banning the usage of ChatGPT to Stack Overflow and Reddit banning solutions and artwork generated from LLMs. Whereas many functions are strictly restricted to writing textual content, just a few functions discover the patterns to work on code, as effectively. The hype surrounding these functions 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 era and automation instruments for years, on this publish I’ll discover what new developments in AI and LLMs imply for software program growth.
Learn the publish in its entirety.
#6 The way to Use Docker and NS-3 to Create Real looking 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 onerous to do with actual gadgets. For instance, some {hardware} will be onerous to get, costly to arrange, or past the talents of the group to implement. When the underlying {hardware} shouldn’t be a priority however the important features that it does is, software program generally is a viable various.
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 circumstances, nevertheless, it isn’t enough to simulate a community. A simulator may want to check how information behaves in a simulated community (i.e., testing the integrity of Consumer Datagram Protocol (UDP) visitors in a Wi-Fi community, how 5G information propagates throughout cell towers and consumer gadgets, and so forth. NS-3 permits such sorts of simulations by piping information from faucet interfaces (a characteristic of digital community gadgets supplied by the Linux kernel that move ethernet frames to and from consumer area) into the operating simulation.
This weblog publish presents a tutorial on how one can transmit dwell information 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 complicated setups and make repeatable simulations in seconds.
Learn the publish in its entirety.
#5 5 Challenges to Implementing DevSecOps and The way to Overcome Them
by Joe Yankel and Hasan Yasar
Traditionally, software program safety has been addressed on the mission degree, emphasizing code scanning, penetration testing, and reactive approaches for incident response. Just lately, nevertheless, the dialogue has shifted to this system degree to align safety with enterprise targets. The best end result of such a shift is one during which software program growth groups act in alignment with enterprise targets, 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 deal with safety actions all through all phases of the software program growth lifecycle (SDLC), will help organizations notice this splendid state. Nevertheless, the shift from project- to program-level considering 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 offers actions organizations can take to beat them.
Learn the publish in its entirety.
#4 Utility of Massive 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 similar time, plunging everybody right into a world the place it’s onerous to differentiate the superbly shaped, but generally faux 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 Massive 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 publish in its entirety.
#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 will be efficient in lots of conditions, Rust’s compiler could be very specific on what constitutes good software program design practices. Each time design assumptions disagree with real-world information and assumptions, there’s the potential of safety vulnerabilities–and malicious software program that may make the most of these vulnerabilities. On this publish, we are going to deal with customers of Rust applications, quite than Rust builders. We’ll 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 usually unavailable, in addition to commenting on potential instructions during 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 influence future safety responses, together with through the coordinated vulnerability disclosure strategies advocated by the SEI’s CERT Coordination Heart (CERT/CC). This publish is the second in a collection exploring the Rust programming language. The first publish explored safety points with Rust.
Learn the publish 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, growing, and sustaining phases of software-intensive merchandise. Fashions, the machine-manipulable representations and the merchandise of an MBSE atmosphere, 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 varied stakeholders. The result’s an total discount of growth dangers.
When initially envisioned, the necessities for a product could appear to signify the fitting product for the stakeholders. Throughout growth, nevertheless, 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 now not represented and is now not a sound supply of take a look at circumstances. Many questions come up, comparable to
- 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 monitor 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 targets for strong design is to have an up-to-date single authoritative supply of fact during which 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 set of requirement, specification, and design submodels throughout the product mannequin. The ensuing mannequin can be utilized as a sound 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 speedy 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 are going to be capable to remedy sure issues which might be classically intractable, which means a standard laptop can not full the calculations inside human-usable timescales. Given this computational energy, there’s rising dialogue surrounding the cyber threats quantum computer systems could pose sooner or later. For 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, nevertheless, on how we are going to shield quantum computer systems sooner or later. If quantum computer systems are to grow to be such useful belongings, it’s affordable to mission that they are going to finally 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 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 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.
Learn the publish in its entirety.
Wanting 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.
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