Agentifact assessment — independently scored, not sponsored. Last verified Mar 6, 2026.
PromptArmor
Prompt injection detection service that uses carefully designed LLM prompting strategies to identify and remove injected instructions from agent inputs. Achieves sub-1% false positive and false negative rates on the AgentDojo benchmark using GPT-4o. Publishes security research on real-world indirect prompt injection vulnerabilities in AI tools like Slack AI and Google Antigravity.
Viable option — review the tradeoffs
You're building autonomous agents that consume untrusted data sources (web pages, user documents, API responses, emails) and need to prevent indirect prompt injection attacks from manipulating agent behavior into stealing credentials, exfiltrating code, or bypassing security controls.
Fast detection with minimal latency overhead. Real-world performance depends on injection sophistication—PromptArmor excels at obvious and moderately obfuscated attacks but may struggle with novel attack patterns not seen during its evaluation. The tool is training-free, so it adapts as LLM reasoning improves, but you inherit the LLM's reasoning limitations.
You're a security team or CISO evaluating third-party AI vendors (legal tech, healthcare, enterprise SaaS) and need visibility into their AI security posture, data flows, and exposure to prompt injection risks before integrating them into your workflows.
Comprehensive risk visibility that goes beyond generic AI security checklists. Reports are understandable to non-technical stakeholders (CISOs, legal teams). Turnaround time and pricing not disclosed in public materials; likely requires direct engagement.
Depends entirely on underlying LLM capability
PromptArmor's detection quality is bounded by the LLM it uses (e.g., GPT-4o). If the LLM fails to recognize a novel or adversarially crafted injection, PromptArmor will too. The research explicitly notes that older LLMs were ineffective at this task; future LLM regressions or adversarial attacks designed to fool modern reasoning could degrade performance.
False negatives on adaptive/adversarial attacks
PromptArmor was evaluated against standard benchmarks (AgentDojo, Open Prompt Injection, TensorTrust) but the research acknowledges testing against 'adaptive attacks.' Real-world attackers may craft injections specifically to evade LLM-based detection (e.g., using encoding, obfuscation, or multi-step reasoning). Treat sub-1% false negative rates as a baseline, not a guarantee.
PromptArmor is semantic detection; regex is syntactic. PromptArmor catches obfuscated and context-aware injections; regex catches obvious patterns.
When agents consume unstructured, variable-format data (web pages, PDFs, user documents) and you need to catch sophisticated, hidden injections. When false positives are costly (you can't afford to block legitimate user input).
When data sources are highly structured and predictable, or when you need zero latency overhead. Regex is faster and requires no external LLM calls.
Trust Breakdown
What It Actually Does
PromptArmor detects and removes malicious instructions hidden in text that users send to AI agents, protecting them from injection attacks that could change how the agent behaves.
Prompt injection detection service that uses carefully designed LLM prompting strategies to identify and remove injected instructions from agent inputs. Achieves sub-1% false positive and false negative rates on the AgentDojo benchmark using GPT-4o. Publishes security research on real-world indirect prompt injection vulnerabilities in AI tools like Slack AI and Google Antigravity.
Fit Assessment
Best for
- ✓ai-security
- ✓risk-monitoring
- ✓prompt-protection
Score Breakdown
Protocol Support
Capabilities
Governance
- permission-monitoring
- audit-log
- ai-asset-mapping
- scope-change-alerts