World’s first runtime security for AI Agents

Automated threat and vulnerability management purpose-built for multi-agent systems with access to critical infrastructure

  • Existing security categories help harden systems and secure against attacks on AI, however security gaps do exist.

    For example, existing AI security (e.g. AI firewalls, DLP) focuses on data security and prompt hijacking, but misses the mark on runtime threats such as agent task manipulation, memory corruption/amnesia, context traceability and a slew of runtime challenges. We highlight these gaps in our authorship at OWASP Top 10 for AI Agents (Feb 2025).

  • Detecting and mitigating runtime Agentic threats involves anomaly detection (to check for deviations from use cases), forensically tracing context by reducing attack surface area in realtime, and securing threat vectors specific to use-cases.

    In addition, we provide immediate code patch fixes for vulnerabilities identified.

  • AI safety is impossible to guarantee, but mitigations should aim to raise the cost of successfully attacking a system. Examples: Break-fix cycles that iteratively improve a system’s defenses, and effective regulations that improve collective security posture. Our team of PhDs (with highly cited AI security papers) stay up to date on new threats for your AI agents so you can focus on enabling AI for your teams.

Founded by security researchers and technologists from CISCO, Stanford and BCG 

From the editors and co-authors of OWASP Top 10 for AI Agents (Feb 2025)

Move beyond Data security and AI Firewalls.

Enter runtime security for AI Agents

Agent hijacking (runaway agents)

Agents either mistakenly or maliciously can use high permissions to execute tasks that may not be required or necessary and could be malicious.

Misaligned learning

Agents learns unintented behaviors leading to untrustworthy and unethical actions to achieve goals.

Orchestration loops

Agentic AI systems often act in iterative cycles, where the outcome of one agent's task can inform the next. If not properly controlled, feedback loops can form where agents reinforce incorrect, harmful, or inefficient behaviors.

Context untraceability

Agents’ ability to temporarily assume permissions from multiple users or systems blurs accountability, making it difficult to pinpoint the origin of actions, especially during malicious activity.

Compromised agent supply chain

Compromised agent components along the supply chain, making a vulnerability translate into unintended downstream actions

Context amnesia

Loss of critical context due to short term memory limit (E.g. Amazon Bedrock has 30-min limit) and inconsistent decision-making leading to inability to track anomalies.

Monitor AI Agents with end to end detection and blazing fast remediation

Real time monitoring of AI Agents

Our  TID  approach understands the  business context of the AI Agent and then at runtime adapts the threat detection technique to avoid false positive detections

Risk scoring across multiple vectors

We use derived scores to provide recommendation & remediation  workflows either fully automated or with Human in the Loop to aid the security professional

Automated remediation recommendations

We immediately create the code patch fix in our workflow along with all variants of possible  attacks for the threat vector to mitigate the blast radius of the attack

Book a learning session with us

We’re providing security teams a complimentary ONE hour workshop session on OWASP’s “Top 10 for AI Agents” led by the publication’s editor and author (Marqus CTO Co-founder), with best practices in building AI Agents with access to critical infrastructure and significant autonomy.