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AI Risk

Cascading Security Failures

"Cascading Security Failures. Localised attacks in multi-agent systems can result in catastrophic macroscopic outcomes (Motter & Lai, 2002, see also Sections 3.2 and 3.4). These cascades can be hard to mitigate or recover from because component failure may be difficult to detect or localise in multi-agent systems (Lamport et al., 1982), and authentication challenges can facilitate false flag attacks (Skopik & Pahi...

AI Risk7. AI System Safety, Failures, & Limitations7.6 > Multi-agent risks2 - Post-deployment

Record summary

A quick snapshot of what this page covers.

Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain7. AI System Safety, Failures, & LimitationsThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

"Cascading Security Failures. Localised attacks in multi-agent systems can result in catastrophic macroscopic outcomes (Motter & Lai, 2002, see also Sections 3.2 and 3.4). These cascades can be hard to mitigate or recover from because component failure may be difficult to detect or localise in multi-agent systems (Lamport et al., 1982), and authentication challenges can facilitate false flag attacks (Skopik & Pahi, 2020). Computer worms represent a classic example of a cybersecurity threat that relies inherently on networked systems. Recent work has provided preliminary evidence that similar attacks can also be effective against networks of LLM agents (Gu et al., 2024; Ju et al., 2024; Lee & Tiwari, 2024, see also Case Study 8)."

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.6 > Multi-agent risks
Entity3 - Other
Intent1 - Intentional
Timing2 - Post-deployment
CategoryMulti-Agent Security
SubcategoryCascading Security Failures

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

Research source for this risk, when available.