CoTester — agentic threat model

Not certain from the listing — The specific underlying LLMs are not disclosed. A key threat is adversarial prompt injection via malicious JIRA stories or specifications, which could manipulate the model into generating malicious test scripts or bypassing validation steps.

Not certain from the listing — The data architecture and vector stores used for 'adaptive learning' are not detailed. Threats include data poisoning of the adaptive learning database, which could train the agent to ignore specific UI flaws or security vulnerabilities over time.

The agent uses the 'AgentRx' engine to auto-heal tests and dynamically generate executable test logic. Threats include insecure tool integration where the agent executes arbitrary code in the test environment, or tool misuse where the agent unintentionally modifies production-adjacent systems.

CoTester supports 'enterprise-ready deployment (on-prem / private cloud)'. While this limits public exposure, threats include container escape, privilege escalation, or lateral movement within the private network if the test execution sandbox is compromised.

The agent incorporates 'AI guardrails with human-in-the-loop checkpoints' and auto-logging. Threats include guardrail bypass where malicious test logic is obfuscated to pass human review, or logging blind spots regarding automated UI modifications made by AgentRx.

Not certain from the listing — While the description notes that 'compliance is critical', specific certifications (e.g., SOC2, ISO 27001) are not cited. Threats include unauthorized data access via JIRA API tokens and lack of a tamper-proof audit trail for automated code changes.

Not certain from the listing — No multi-agent orchestration or external agent marketplace interactions are described. General threat: If integrated into a broader CI/CD agent ecosystem, CoTester could suffer from cascading failures or trust abuse from compromised upstream developer agents.