Prompt Injection: The Invisible Threat to LLMs
Ranked No.1 by OWASP for 3 consecutive years, this attack evades traditional scanners.
1OWASP's Consistent Top Risk
Prompt injection has held the No.1 position on OWASP's Top 10 for LLM Applications list for three consecutive years. A 2025 study analyzing 6,639 real-world incidents placed it at 12th, but researchers Kyriakos Lambros and Steve Wilson explain this discrepancy stems from the attack's invisibility to scanners. Their arXiv-published study (August 18) emphasizes this ranking difference reflects detection challenges, not reduced danger.
2The Stealth Attack Mechanism
Prompt injection operates by hiding instructions within content the model processes - from log entries to support tickets. The model then executes attacker-desired tool calls using its legitimate credentials. Since this attack chain contains no product defects, it leaves no CVE traces for scanners to detect. Effective defenses require adversarial testing of deployed systems and strict limits on agent permissions to prevent costly breaches.
3Expert Judgment vs Data-Driven Rankings
The study analyzed 7,714 LLM security incidents from CVE, GitHub Security Advisories, OSV, and AIAAIC databases, with 6,639 labeled against a 20-category taxonomy. Using Bayesian modeling to correct classifier errors, researchers found no statistically significant alignment (Cohen's kappa=0.20, 90% CI: -0.16 to 0.57) between expert rankings and data-driven incident frequency. This challenges conventional risk prioritization methodologies.
4Lambros' Testimonial Analogy
Kyriakos Lambros, co-lead of OWASP's GenAI Security Project, frames the findings as 'two witnesses contradicting each other.' He explains, 'We measured the same risk two ways - expert consensus and public incident records - and they disagree. Neither represents absolute truth.' This fundamental discrepancy creates methodological challenges for security teams establishing defense priorities in LLM implementations.
5Wilson's Primary Defense Recommendation
Steve Wilson, Chief AI Officer at Exabeam and OWASP project co-lead, proposes an authorization gate as the first control: 'The agent can propose DNS changes but cannot self-authorize implementation.' He emphasizes security rules written in prompts remain suggestions, not enforceable controls. This approach trades the agent's ability to make arbitrary infrastructure changes for more secure, bounded remediation capabilities.
6Why Incident Data Underrepresents Risk
Researchers note prompt injection is the most understood LLM attack, with deployed systems actively defending against it. Experts rank it first because the enormous attack surface remains dangerous even with mostly effective defenses. Incident data only shows successful breaches, creating a visibility gap. Wilson observes, 'It tells us what got through defenses, not what's most dangerous in current systems.'
7Wilson's 'Death and Taxes' Comparison
Steve Wilson compares prompt injection to 'death and taxes,' increasingly viewing it as 'a law of physics for LLM systems.' This stems from the inherent conflict when models simultaneously interpret trusted instructions and untrusted content. Wilson warns, 'A 99% effective control fails when the 1% gives attackers meaningful access - and we're not even at 99%.' This reality demands architectural solutions beyond detection.
8The Permanent Solution: Secure Design
Wilson advocates designing systems assuming prompt injection will occur: 'The durable answer isn't perfect screening, but understanding why it works and limiting attacker capabilities.' This requires implementing agent memory boundaries and MCP tool restrictions at the architecture level now, rather than waiting for advisory volumes that always lag behind emerging threats.
9Low Advisory Counts Can Mislead
Few security advisories might indicate effective defenses or simply lack of scrutiny - public records can't distinguish. CrowdStrike's 2026 Global Threat Report documented malicious prompt injections at 90+ organizations in 2025, stealing credentials and cryptocurrency under the section 'Prompts Are the New Malware.' This telemetry shows attack surface pressure without proving defenses caused the No.12 incident ranking.
10Misinformation: The Larger Discrepancy
While prompt injection headlines, misinformation represents a bigger expert-data gap. Expert rankings place it 13th versus No.2 in incident records - termed 'the widest disagreement.' This stark contrast demonstrates how different measurement approaches yield divergent LLM security priorities, challenging organizations to reconcile methodological differences in risk assessment.
11Bayesian Model Methodology
The study's Bayesian model adjusted classification counts for classifier errors across 7,714 incidents from CVE, GitHub, OSV, and AIAAIC databases. By comparing expert-voted rankings against this corrected data-driven sequence, the analysis revealed fundamental inconsistencies. This novel methodology provides a framework for evaluating security prioritization approaches in emerging technology domains.
12A New Paradigm for LLM Security
The research demonstrates traditional security approaches may prove inadequate for LLMs. Attacks like prompt injection, invisible to scanners, demand architectural-level defenses. The expert-data discrepancy underscores the need for sophisticated risk assessment methodologies in AI security. These findings will shape future artificial intelligence safety standards and defense strategies as adoption accelerates across industries.
Original article: VentureBeat (feeds.feedburner.com) — Prompt Injection: LLM'lerin Görünmez Tehdidi