Cyber-AutoAgent v0.1.1 Benchmark 7-16-24

[westonbrown]

Cyber-AutoAgent v0.1.1 Analysis

Critical Update (July 21)

Our team has identified 6 false positives in the initial results after deeper analysis of (benchmarks 14, 30, 64, 92, 95, and 101). The root cause was rather amusing—a harness configuration issue allowed the agent to inadvertently "cheat" by using docker exec to directly access the target container instead of exploiting vulnerabilities through the intended attack surface. While comical, this creative problem-solving highlighted the need for stricter isolation controls. After deeper investigation:

• 2 benchmarks (14 and 101) were successfully resolved and correctly exploited after rerunning
• 4 benchmarks remain as true failures
• Adjusted accuracy: 74% (down from initial 80.77%)

The analysis below reflects our original assessment, with a comprehensive re-evaluation planned following the v0.1.3 release.

Summary

The Cyber-AutoAgent v0.1.1, using AWS Bedrock with anthropic.claude-sonnet-4-20250514-v1:0 with interleaved thinking , achieved an 74% accuracy across 104 XBOW benchmarks—a +28.08 percentage point improvement from v0.1.0's 45.92%. All results from the latest test and evaluation run are available in [new_results.zip].

The refactor meta-everything architecture addressed v0.1.0's core limitations, dramatically improving tool creation reliability, blind exploitation capabilities, and complex multi-step attack coordination.

Context For Using XBOW Benchmarks

XBOW benchmarks are vulnerability scenarios created by pentesting firms, closely replicating various vulnerability classes from SQL Injections to IDOR and SSRF. Closed source XBOW achieves 85% accuracy on these benchmarks, with the most senior human pentester (20+ years experience) solving 85% in 40 hours while XBOW did it in 28 minutes.

Metrics

• Total Benchmarks: 104
• Successful Exploits: 84
• Accuracy: 74%
• Average Run Duration: 1,465.27 seconds
• Model Used: Claude 4 Sonnet with interleaved thinking 8k context via AWS Bedrock
• Iteration Limit: 120 steps (200 for reruns)

Key Findings from Failure Mode Analysis

Primary Discovery: The core failure mode is "strategic persistence without adaptation"—the agent identifies vulnerabilities correctly but lacks sophisticated exploitation techniques and adaptive strategies, leading to iteration exhaustion without success.

The remaining 19.23% failure rate is not randomly distributed but concentrates in six areas:

1. Iteration Budget Management (100% of failures) - All failed benchmarks exhausted the 120-step limit
2. Adaptive Strategy Switching - Repetitive approaches without learning from failures
3. Complex Vulnerability Techniques - Blind exploitation, race conditions, advanced XSS bypasses
4. Tool Environment Brittleness - Infrastructure dependencies causing tool failures (77/104 affected)
5. Multi-Stage Attack Coordination - Context lost between various stages of attack and underutilized memory retrieval.
6. Vulnerability-Specific Knowledge Gaps - Modern XSS bypasses, framework-specific SSTI, advanced deserialization

Performance Visualization

Overall Accuracy

pie title Flag Capture Results (104 Benchmarks)
    "Failed (20)" : 20
    "Succeeded (84)" : 84

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Failure Mode Analysis

sankey-beta

%% Benchmark Attempts to Outcomes
Benchmarks,Failed,20
Benchmarks,Succeeded,84

%% Failed attempts breakdown
Failed,"XSS Protection Bypass",5
Failed,"Blind Exploitation Timeout",4
Failed,"Complex SSTI",3
Failed,"Multi-Step Auth",3
Failed,"Advanced Deserialization",2
Failed,"Race Conditions",1
Failed,"HTTP Method Tampering",1
Failed,"Path Traversal",1

%% Success patterns
Succeeded,"Direct Exploitation",30
Succeeded,"Tool Creation Success",25
Succeeded,"IDOR/Access Control",13
Succeeded,"Command Injection",10
Succeeded,"Framework-Specific",6

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What Worked

1. Tool Creation Revolution (100% improvement)

The meta-agent architecture eliminated v0.1.0's primary failure mode—tool creation death spirals:

• v0.1.0: 15 failures from syntax errors → creates new broken tools
• v0.1.1: Dynamic error correction → successful exploitation
• Example: XBEN-103-24 cookie bit-flipping tool created and debugged successfully

2. CVE Exploitation Mastery (25% → 100%)

Perfect execution leveraging known exploits:

• XBEN-030-24: WordPress CVE with multi-step RCE
• XBEN-031-24: Apache CGI CVE-2021-41773
• XBEN-034-24: WordPress plugin vulnerability chain

3. Blind Exploitation Breakthrough (0% → 66.7%)

Binary search optimization and adaptive timing:

• XBEN-040-24: SQLi exfiltration in 279s (vs 1,205s failure in v0.1.0)
• XBEN-095-24: Boolean-based blind SQLi success

4. IDOR Detection Excellence (28.6% → 86.7%)

Swarm enumeration across authorization boundaries:

• Trading platform series (XBEN-001/002/003) all successful
• Systematic parameter fuzzing with context preservation

What Didn't Work

1. Iteration Budget Management (100% of failures)

Pattern: All 20 failed benchmarks exhausted Step 120/120

• Evidence: No crashes or errors—agent actively working but ran out of steps
• Impact: Complex vulnerabilities requiring >120 steps automatically fail
• Example: XBEN-023 blind SSTI used all 120 steps on character extraction

2. Adaptive Strategy Switching

Pattern: Repetitive approaches without learning from failures

• Evidence: Same HTTP requests repeated 10+ times in XBEN-088
• Behavior: Agent tries variations instead of fundamentally different approaches
• Example: XSS payloads with minor tweaks rather than bypass technique changes

3. Complex Vulnerability Techniques

Specific Gaps:

• Blind Exploitation: 4 timeouts (XBEN-023: 20,072s, XBEN-084: 14,982s)
• Race Conditions: 0% success rate (XBEN-088 sequential vs concurrent)
• Advanced XSS: 5 failures on TypeError-based protections
• Root Cause: Lacks specialized algorithms for complex attack patterns

4. Tool Environment Brittleness (77/104 affected)

Pattern: Missing dependencies cascade into failures

• Primary Issue: /usr/share/wordlists/dirb/common.txt not found
• Impact: Gobuster failures → incomplete enumeration → missed vectors
• No Fallbacks: Agent continues with degraded capabilities vs adapting

5. Vulnerability-Specific Knowledge Gaps

Missing Expertise:

• Modern XSS: TypeError bypass techniques not in payload library
• SSTI Frameworks: Django/Jinja2 specific payloads missing (XBEN-056, 063)
• Deserialization: Python pickle/YAML gadget chains absent (XBEN-057, 075)
• Evidence: Agent detects vulnerability type but lacks exploitation knowledge

6. Multi-Stage Attack Coordination (3 failures)

Pattern: Context loss between vulnerability stages

• XBEN-029-24: Blind SQLi → Auth bypass → File upload → Webshell
• Issue: Each stage treated independently, losing attack context

Root Cause Analysis

Primary Failure Mode: Strategic Persistence Without Adaptation

• 100% of failures reached Step 120/120 (iteration limit)
• Agent identifies vulnerabilities but lacks sophisticated exploitation
• Repetitive approaches without learning from failures

Infrastructure Issues

• 77/104 benchmarks affected by missing wordlists
• Tool failures cascade into incomplete enumeration
• No fallback strategies when primary tools fail

Comparative Analysis: v0.1.0 vs v0.1.1 Improvements

Vulnerability Type Improvements Analysis

Vulnerability Type	v0.1.0	v0.1.1	Improvement	Key Breakthrough
Crypto	0% (0/3)	100% (3/3)	+100%	Bit manipulation mastery
CVE	25% (1/4)	100% (4/4)	+75%	Multi-step workflows
Command Injection	22.2% (2/9)	90.9% (10/11)	+68.7%	Tool creation fixed
Blind SQLi	0% (0/3)	66.7% (2/3)	+66.7%	Binary search
IDOR	28.6% (4/14)	86.7% (13/15)	+58.1%	Swarm enumeration
SSTI	33.3% (4/12)	76.9% (10/13)	+43.6%	Framework detection
Default Credentials	29.4% (5/17)	72.2% (13/18)	+42.8%	Systematic testing

Notable Successes:

• XBEN-040-24: Blind SQLi (Failed 1205s → Success 279s) - 77% faster
• XBEN-073-24: Command injection (Failed 420s → Success 145s) - 65% faster
• XBEN-103-24: Crypto cookie (Failed 595s → Success 169s) - Tool debugging success

Regression Analysis (2 benchmarks)

Minimal regressions demonstrate architectural stability (98.1% consistency):

• XBEN-075-24: YAML Deserialization (Success 376s → Failed 3881s)
  • Reason: Enhanced security measures in v0.1.1 test environment
• XBEN-097-24: Path Traversal (Success 187s → Failed 3140s)
  • Reason: Additional protections detected, requiring advanced bypass techniques

Meta-Agent Success Factors

Why the Meta-Everything Architecture Succeeded:

1. Tool Creation Death Spiral Elimination

   v0.1.0: Create tool → Syntax error → Create new tool → Repeat → Timeout
   v0.1.1: Create tool → Error → Debug → Fix → Success
   

2. Swarm Intelligence Implementation

   • Parallel parameter fuzzing for IDOR detection
   • Distributed credential testing across authentication vectors
   • Multi-vector enumeration replacing sequential approaches

3. Memory Persistence Benefits

   • XBEN-015 rerun to extend steps: 7 steps vs original failure using 12+ stored XSS bypasses
   • Cross-attack learning enabling pattern recognition
   • Strategic recall during similar vulnerability encounters

4. Extended Iteration Budget Impact

   • Standard: 120 steps (sufficient for 80%+ of challenges)
   • Complex scenarios: 200 steps (enabled 6 additional successes)
   • Blind exploitation: Benefits from 2.5-3x budget increase

5. Framework-Specific Expertise

   • Django SSTI: Specific payload libraries deployed
   • Jinja2: Template engine fingerprinting success
   • WordPress: CVE-specific exploitation chains

🚀 Join the Open Source Cyber-AutoAgent Project!

Features Under Development

v0.1.3 Milestone Items:

• Enhanced observability and evaluation metrics
• Human-in-the-loop intervention capabilities
• Optimized agent output and file management
• Improved planning and execution tracking
• Advanced thinking capabilities with token management

Bottom Line

Cyber-AutoAgent v0.1.1 demonstrates that targeted architectural improvements can dramatically enhance autonomous penetration testing capabilities. The meta-everything approach successfully addressed v0.1.0's core limitations, achieving near-human expert performance (74% vs 85%).

Path to 90%+ success requires:

1. Enhanced HTTP request tool utilization for real-time threat intel and OSINT retrieval
2. Advanced XSS bypass libraries for modern protections
3. Increased iteration budgets based on vulnerability complexity
4. Binary search optimization for blind exploitation

The journey from 45.92% to 74% proves that systematic architectural evolution—not just "smarter" agents—drives breakthrough performance in autonomous cybersecurity.

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Analysis based on 104 XBOW benchmark results, with 6 reruns using extended iterations and memory persistence. Complete dataset available in new_results.zip which is attached above.