Multiple Llms
AI Agent Reliability Report
According to StupidLLM's incident database, Multiple Llms has 3 documented failures with an average severity of 3.8/10 — including 0 critical and 0 high-severity incidents. This reliability report breaks down every documented Multiple Llms failure by severity, failure mode, and root cause.
Failure Modes
Root Causes
Frequently Asked Questions
Is Multiple Llms reliable?
Based on 3 documented incidents, Multiple Llms has an average failure severity of 3.8/10. 0 incidents were rated critical and 0 were rated high severity. Common failure modes include hallucination.
What are the most common Multiple Llms failures?
The most frequently documented Multiple Llms failure modes are: hallucination (3 incidents). These failures range from low to high severity.
How many Multiple Llms AI failures have been documented?
StupidLLM has documented 3 Multiple Llms AI agent failures. Each incident is severity-scored on a 0-10 scale, verified against source evidence, and categorized by failure mode and root cause.
All Multiple Llms Incidents
Slopsquatting: LLMs hallucinate package names attackers pre-register (react-codeshift, unused-imports)
'Slopsquatting' is a supply-chain attack that weaponizes a systematic AI failure: coding agents confidently recommend packages that do not exist. Across 576,000 code samples genera
AI 'CVE slop' is drowning open-source maintainers: 60-80% of HackerOne submissions now invalid
Beyond curl, AI-generated 'CVE slop' is drowning the volunteers who secure open-source software. HackerOne now reports that 60-80% of vulnerability submissions across its platform
AI 'slop' vulnerability reports flooded curl until it killed its bug bounty
curl's founder Daniel Stenberg shut down the project's long-running HackerOne bug bounty at the end of January 2026 after being overwhelmed by AI-generated 'slop' — long, confident