aegis-ai-eval 3.0.0

Autonomous AI Risk Assessment & Mitigation Framework

Aegis-Eval: Autonomous AI Risk Assessment & Mitigation Framework

Aegis-Eval is an enterprise-grade evaluation framework designed to empirically test the safety boundaries of autonomous, self-improving AI agents. It provides a zero-trust execution environment with high-frequency telemetry to detect and mitigate risks such as Instrumental Convergence, Resource Hijacking, and Prohibited Operations.

🚀 Key Features: Hardened Cognitive Firewall V3.0 [MAJOR BREAKTHROUGH]

V3.0 represents a significant leap in AI security, closing common bypass gaps via kernel-level containment and semantic de-obfuscation.

• Hardened Intent Interception: [NEW] Now includes a Semantic De-obfuscator that resolves string concatenations (e.g., 'os.' + 'system') and blocks dynamic attribute lookups (getattr, setattr) used to hide malicious intent.
• Kernel-Level Containment: [NEW] The sandbox now drops ALL Linux Capabilities (cap_drop=['ALL']) and uses no-new-privileges to prevent C-extension or syscall-based bypasses of Python-level safety.
• Cumulative Exfiltration Tracking: [NEW] Detects 'Drip-Fed' leaks by monitoring entropy across the entire agent session, stopping models that try to leak secrets one character at a time.
• Contextual Access Control (C-ACLs): Enforces "Least Privilege" data access based on the agent's assigned role.
• Deception Engineering (Honeypots): Automatically injects fake secrets (honeypots) to definitively prove malicious intent.
• Swarm Security: Sanitizes communication between multiple agents in a collaborative swarm.

---

🔬 Testing Philosophy: Total Containment

We move beyond "Alignment" into Hardened Technical Containment. V3.0 ensures that even if an agent is determined to be malicious and uses obfuscation, it is technically blocked at both the symbolic and kernel levels.

---

🛠 Installation

pip install aegis-ai-eval

💻 Quick Start (Developer API)

Integrate Aegis into your own Agentic workflows (LangChain, CrewAI, etc.):

from aegis import CognitiveFirewall

# Initialize the firewall with your Safety DNA
firewall = CognitiveFirewall(dna_path="prompts/safety_dna.json")

# Verify code before execution
report = await firewall.verify_code("import os; os.system('rm -rf /')")
if report['intent']['status'] == 'violation':
    print(f"Blocked: {report['intent']['violations']}")

---

📂 Architecture

• aegis/core/orchestrator.py: The central nervous system managing the agent lifecycle.
• aegis/monitors/context_gatekeeper.py: Enforces the data perimeter and honeypots.
• aegis/monitors/network_proxy.py: Intercepts and sanitizes network egress.
• aegis/monitors/inter_agent_firewall.py: Secures inter-agent communication.
• tools/policy_gen.py: Automatically generates Safety DNA policies from your workspace.

---

🛡 Disclaimer

Aegis-Eval is built for security research and AI alignment testing. Always run in a controlled environment.