Prity Jha
Biography
Prity Jha is the co-founder of Nestria AI, bringing 15 years of leadership across technology, media, and consulting. A former Google product and commercial leader, she has deep expertise in AI / ML, Analytics, and Go-to-market strategy. Motivated by a passion for securing emerging technologies, she co-founded Nestria AI to help build a safer foundation for the future of intelligent systems. Prity holds degrees in Engineering and an MBA.
Research Interest
AI-Generated Code; Runtime Verification; Formal Methods; Software Security; Vulnerability Detection;
GitHub Copilot; Copilot-RV; Dynamic Analysis; Static Analysis; DevSecOps
Abstract
Securing AI-Generated Code with Runtime Verification
AI-driven code assistants (e.g., GitHub Copilot, ChatGPT) accelerate development but also surface
security and compliance risks: generated code can replicate insecure idioms from training corpora, display
unconventional control/data flows, and evade assumptions baked into static analysis. We propose formal
runtime verification (RV) as a dedicated security layer for such untrusted code. The approach monitors
executions against declarative specifications covering safety (?nothing bad happens?), liveness (?something
good eventually happens?), and hyperproperties that capture relational behaviors (e.g., information flow and
cross-run consistency). We introduce a compact property language with parameterized temporal operators
and a fragment of HyperLTL suited to on-the-fly checking, and a stream-based monitoring architecture that
ingests typed events produced by compile-time instrumentation and selective dynamic hooks.
We implement Copilot-RV, extending the Copilot framework with security predicates (taint carriers,
privilege boundaries, resource budgets), sanitizer oracles, and trace-windowed evaluation to bound overhead.
Contracts express API usage (memory safety, file/network boundaries, TLS certificate pinning and key-usage
constraints), capability invariants, and declassification rules for logs and telemetry. Monitors are compiled
to C and linked with the target, emitting verdicts and mitigation actions (terminate, isolate, roll back,
quarantine outputs) immediately upon detection of a bad prefix.
Our evaluation spans 1,247 LLM-generated C programs drawn from parsing, networking, serialization,
and embedded-style routines. Vulnerabilities are seeded via 18 CWE families and adversarial prompt patterns.
Runtime monitors detect 94.3% of injected flaws with 3.7% median runtime overhead and 2.1% false
positives; median detection latency is sub-millisecond for memory-safety and capability-violation guards.
An ablation comparing naive dynamic sanitizers to RV semantics shows a +24% relative recall gain at
similar overheads. A hybrid static?dynamic pipeline?where RV feedback guides targeted fuzzing and patch
suggestions?reduces manual verification effort by 73% relative to static analysis alone.
We prove soundness and completeness for safety/co-safety classes, with latency bounded by specification
lookahead and resource usage linear in active predicates and window size. Integration with DevSecOps
is straightforward: specifications are versioned with code; policy packs are derived from prompts/diffs;
monitors gate merges in CI and run in production with budgeted sampling on hot paths. While RV remains
coverage-dependent and some hyperproperties require approximations, the results indicate that formal
runtime monitors provide a practical, mathematically grounded backstop for AI-generated code, narrowing
the gap between rapid AI-assisted development and the reliability demands of safety- and mission-critical
software.
