Skip to main content
MIT AI Risk Initiative
BackReal-world risks (inducing traditional economic and social security risks)
Home/Risks/National Technical Committee 260 on Cybersecurity (TC260) (2024)/Real-world risks (inducing traditional economic and social security risks)
Cyberspace risks (Risks of security flaw…
Real-world risks (Risks of using AI in i…

Real-world risks (inducing traditional economic and social security risks)

AI Safety Governance Framework

National Technical Committee 260 on Cybersecurity (TC260) (2024)

Source
Sub-category
Risk Domain
7AI System Safety, Failures & Limitations
7.3Lack of capability or robustness

AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.

"Hallucinations and erroneous decisions of models and algorithms, along with issues such as system performance degradation, interruption, and loss of control caused by improper use or external attacks, will pose security threats to users' personal safety, property, and socioeconomic security and stability."(p. 11)

Entity— Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent— Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing— Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Other risks from National Technical Committee 260 on Cybersecurity (TC260) (2024) (25)

Risks from models and algorithms (Risks of explainability)

7.4 Lack of transparency or interpretability
AI systemUnintentionalOther

Risks from models and algorithms (Risks of bias and discrimination)

1.1 Unfair discrimination and misrepresentation
HumanOtherPre-deployment

Risks from models and algorithms (Risks of robustness)

7.3 Lack of capability or robustness
AI systemOtherPost-deployment

Risks from models and algorithms (Risks of stealing and tampering)

2.2 AI system security vulnerabilities and attacks
OtherOtherOther

Risks from models and algorithms (Risks of unreliable output)

3.1 False or misleading information
AI systemUnintentionalPost-deployment

Risks from models and algorithms (Risks of adversarial attack)

2.2 AI system security vulnerabilities and attacks
HumanIntentionalPost-deployment
View all 25 risks from this paper →