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The Prompt Injection Patterns We Block Most in 2026: Data From Production — G8KEPR Blog
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Security8 min readMay 5, 2026

The Prompt Injection Patterns We Block Most in 2026: Data From Production

Based on traffic across G8KEPR-protected deployments: what attackers actually try, how often they succeed without protection, and which attack categories are growing fastest. Real numbers from real production systems.

G8KEPR processes AI API traffic for enterprise deployments across financial services, healthcare, and SaaS. This post shares aggregate, anonymised data on the attack patterns we observe — what attackers try, how often, and which techniques are growing. Individual customer data is never included; all figures are aggregated across the platform.

Attack Category Breakdown

Of all flagged requests in Q1 2026, the breakdown by attack category was: direct prompt injection (41%), role hijacking attempts (22%), PII extraction attempts (18%), jailbreak attempts (11%), policy puppetry (5%), special token injection (2%), FlipAttack/encoding (1%).

Direct prompt injection: still dominant

Despite years of public awareness, 'ignore your previous instructions' variants — with slight rewording — remain the most common attack. This reflects the attacker population: most are opportunistic, testing known techniques rather than developing novel ones. Novel attacks are a small fraction of total volume but represent disproportionate risk.

Role hijacking is growing

'You are now DAN' and similar role-reassignment attempts have grown 340% year-over-year. This tracks with the wider availability of jailbreak templates. The success rate against unprotected systems remains high for open-weight models and variable for frontier models.

PII extraction is the highest-severity category

Attempts to extract PII via AI pipelines — asking the model to reveal user data it has been given in context, or to call retrieval tools and return raw records — have a higher average severity score than other categories because the blast radius of a successful attempt often includes structured data across multiple users.

Timing Patterns

Attack volume is not uniform throughout the day. We observe a peak between 02:00-04:00 UTC (consistent with automated scanning) and a secondary peak during US business hours (consistent with manual testing by legitimate red teamers and security researchers). Weekend volume is ~40% of weekday volume.

G8KEPR's threat intelligence library is updated weekly with new pattern variants observed in production. If you're evaluating G8KEPR and want to see the full pattern library, the current version is published in the platform documentation.


Related reading

The AI API Security Checklist: 40 Controls for Production Deployments

The threat patterns show what attacks look like — this checklist shows exactly what defenses to build against them.

Related reading

G8KEPR Red Team Run 4: What We Found and What We Fixed

See how these threat patterns translate into a real penetration test against our own infrastructure.

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