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Policy Enforcement via Program Monitoring

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  1. Policy Enforcement via Program Monitoring Jay Ligatti (Princeton); joint work with: Lujo Bauer (CMU), David Walker (Princeton)

  2. Security Policy Enforcement • News flash:Software sometimes does bad stuff • Bugs • Malicious design • One mitigation is run-time monitoring • Ensure that software adheres to run-time constraints specified by a security policy • Stack inspection, access-control lists, applet sandboxing, firewalls, resource monitors, …

  3. Run-time Program Monitors Untrusted Target Program Monitor Executing System Open(f,“w”) Open(f,“w”) Open(f,“w”) is OK • Monitors enforce policies by: • Interposing between untrusted code and the system executing the untrusted code • Making sure only legal code is executed

  4. Policies Become More Complex • As software becomes more sophisticated • Multi-user and networked systems • Electronic commerce • Medical databases (HIPAA) • As we tighten overly relaxed policies • Insecure default configurations disallowed • Downloading .doc files requires warning • As we relax overly tight policies • All applets sandboxed (JDK 1.0) vs. only unsigned applets sandboxed (JDK 1.1)

  5. Research Questions/Goals • What are the limits of program monitors?Which policies can they enforce? • Understanding limits can save us from trying to enforce policies with insufficiently strong mechanisms • Also helps us choose best mechanism to enforce policy at hand • How should we design a language in which to write complex and dynamic policies?

  6. Outline • Motivation • Which policies can monitors enforce? • Language for writing complex policies? • Delineating the enforceable policies • Formally define “monitor enforces a policy” • Analysis of enforcement powers • A language for writing complex policies • Handle complexity with composition • Polymer language • Summary

  7. Local Roadmap 1. Define policies (and properties) on systems 2. Define monitors and their operational semantics 3. Define enforcement of a policy by a monitor

  8. Systems and Executions • System = a state machine that transitions states by executing actions • We specify a system according to the possibly countably infinite set of actions it can execute A = { open f, close f } (where f is any file name) • Execution = possibly infinite sequence of actions open f; close f open f1; close f1; open f2; close f2; …

  9. Execution Notation • On a system with action set A,A* = set of all finiteexecutionsAω = set of all infiniteexecutionsA∞ = set of allexecutions • The empty sequence (execution) is ● • The concatenation of sequences s and u is s;u • Prefix notation: s≤u (or u≥s) • Means: s is a finite prefix of possibly infinite u • Read: “s prefixes u” (or “u extends s”)

  10. Policies [Schneider ’00] • Policy = predicate P on sets of executions • Set of executions SÍA∞satisfiesP iff P(S) • Termination policy • P(S) iff every execution in S is finite • Key uniformity policy • P(S) iff the cryptographic keys used in all executions in S form uniform distribution

  11. Properties [Schneider ’00] • Policy P is a property iff $ predicate p on executions such that "SÍA∞ , P(S) Û "uÎS : p(u) • Properties cannot specify relationships between executions • Termination is a property • Key uniformity is not a property • Monitors in our model only see individual executions, so they enforce properties

  12. Safety and Liveness [Lamport ’77; Alpern, Schneider ’85] • Two types of properties have been studied a lot • Safety: “Bad executions cannot be made good” "sÎA∞ : Øp(s) Þ$s’≤s : "u≥s’ : Øp(u) • Access-control (cannot “undo” illegal accesses) • Liveness: “Finite executions can be made good”"sÎA* : $u≥s : p(u) • Termination and nontermination

  13. Local Roadmap 1. Define policies (and properties) on systems 2. Define monitors and their operational semantics 3. Define enforcement of a policy by a monitor

  14. Operation of Monitors: Accepting an OK Action Untrusted Target Program Monitor Executing System Open(f,“w”) Open(f,“w”) Open(f,“w”) is OK Monitor inputs actions from target and outputs actions to the executing systemHere, input action is safe to execute, so monitor accepts it (makes it observable)

  15. Operation of Monitors: Suppressing an Action Untrusted Target Program Monitor Executing System Open(f,“w”) Open(f,“w”) is not OK Input action is not safe to execute, so monitor suppressesit and allows target to continue executing

  16. Operation of Monitors: Inserting an Action Untrusted Target Program Monitor Executing System Open(f,“w”) Close(f,“w”) Open(f,“w”) is not OK Input action is not safe to execute, so monitor inserts another action, then reconsiders the original action

  17. Modeling Monitors • Model a monitor that can accept, suppress, and insert actions as an edit automaton (Q,q0,t) • Q is finite or countably infinite set of states • q0 is initial state • A complete, deterministic, and TM-decidable function t : Q x A ® Q x (A U {●}) suppress trigger action current state input (trigger) action new state action to insert

  18. Operational Semantics • A monitor’s transition function defines how it behaves on individual input actions • For the definition of enforcement, we need to consider the way monitors transform entire input executions Monitors are execution transformers Valid output Untrusted input a1;a2;a2;a3;… a1;a2;a2;a4;… Monitor

  19. Operational Semantics Judgments • Desired judgment: (q0,s) X ßu • Automaton X starting in state q0transforms input sequence s into output sequence u • Build up to this judgment 1. Single-step judgment (q,s) X ®u (q’,s’) 2. Multi-step judgment (q,s) X Þu (q’,s’) 3. Transforms judgment (q0,s) X ßu

  20. Local Roadmap 1. Define policies (and properties) on systems 2. Define monitors and their operational semantics 3. Define enforcement of a policy by a monitor

  21. Effective Enforcement • Effective enforcers adhere to two principles • Soundness: • Monitors’ outputs (i.e., observable executions) must satisfy the desired property • Transparency: • Monitors must not alter the semantics of valid inputs • Conservative definition: on a valid input sequence s, a monitor must output s

  22. Enforcing Properties • Automaton X starting in q0 enforcesp on a system with action set A iff "sÎA∞: $uÎA∞:1. (q0,s) X ßu2. p(u) [Soundness]3. p(s) Þ (s=u) [Transparency]

  23. Outline • Motivation • Which policies can monitors enforce? • Language for writing complex policies? • Delineating the enforceable policies • Formally define “monitor enforces a policy” • Analysis of enforcement powers • A language for writing complex policies • Handle complexity with composition • Polymer language • Summary

  24. Enforcement Powers Related Work • Previous work has examined the enforcement bounds of monitors [Schneider ’00; Viswanathan ’00; Hamlen, Morrisett, Schneider ’03; Fong ’04] • In the previous models, monitors respond to dangerous actions by halting the target outright • Our framework considers more expressive monitors that can accept, suppress, and insert actions

  25. Enforcing Properties with Edit Automata • Powerful enforcement technique • Suppress (feign execution of) potentially bad actions, and later, if the suppressed actions are found to be safe, re-insert them • Limitation: model assumes monitors have same computational power as observing system • In practice, some actions cannot be feigned: • Actions requiring an outside system to execute • Time-dependent actions

  26. Renewal Property Definition • Technique of suppressing dangerous actions allows edit automata to enforce any reasonable infinite renewalproperty • Renewal: “Infinite executions are good iff they are good infinitely often” "sÎAω : p(s) Û {u≤s | p(u)} is an infinite set

  27. Example Renewal Property • Check subsystem configuration after rebooting: Whenever reboot is called, it must be followed by a call to checkConf • Is a renewal property: • A valid infinite execution has infinitely many valid prefixes (in which checkConf always follows reboot) • An invalid infinite execution has only finitely many valid prefixes • There must be some invalid prefix where thenext action after a reboot is not checkConf — all successive prefixes are invalid

  28. Edit Automata Enforcement(Lower Bound) • Theorem: " properties p such that 1. p is a renewal property, 2. p(●), and 3. "sÎA* : p(s) is decidable, $ an edit automaton that enforces p. Edit automata can enforce any reasonable renewal property

  29. Edit Automata Enforcement(Lower Bound) • Proof idea: Technique of suppressing actions until they are known to be safe causes every valid prefix, and only valid prefixes, of the input to be output • Given a renewal property p, construct an edit automaton X that uses this technique • In all cases, X correctly enforces p • If input s has finite length, X outputs longest valid prefix of s • Else if Øp(s) and s is infinite, X outputs the longest valid (finite) prefix of s (since p is a renewal property) • Else if p(s) and s is infinite, X outputs every prefix of s and only prefixes of s

  30. Edit Automata Enforcement (Precise Bounds) • An edit automaton can only enforce reasonable renewal properties, except for a corner case that allows some valid infinite-length executions to have only finitely many valid prefixes

  31. Enforcing “Almost Renewal” Properties Valid output Untrusted input a1;a2;a2;a3;… Monitor Mr. Monitor: I’m examining action a1. a1 is valid. Valid output Untrusted input a1;a2;a2;a3;… a1 Monitor

  32. Enforcing “Almost Renewal” Properties Valid output Untrusted input a1;a2;a2;a3;… a1 Monitor Mr. Monitor: I’m examining action a2. a1;a2 is invalid, and the only valid extension of a1;a2 is a1;a2;a2;a2;… I’ll just infinitely insert a2. Valid output Untrusted input a1;a2;a2;a3;… a1;a2;a2;a2;a2;… Monitor

  33. Edit Automata Enforcement (Precise Bounds) • Enforcing an “almost renewal” property requires automaton having already input sequence s to be able to decide: • only one extension s’ of s is valid • s’ has infinite length • how to compute the actions in s’

  34. Safety, Liveness, Renewal • Let us compare the set of renewal properties with the well-established sets of safety and liveness properties • Safety: “Bad executions cannot be made good” • Liveness: “Finite executions can be made good” • Renewal: “Infinite executions are good iff they are good infinitely often”

  35. Example Renewal Property, Revisited • Check subsystem configuration after rebooting: Whenever reboot is called, it must be followed by a call to checkConf • Is not a safety property: Øp(reboot) but p(reboot;checkConf) • Is not a liveness property: reboot;a cannot be “made good” when a¹checkConf

  36. Safety, Liveness, Renewal All Properties 1 File access control 2 Trivial 3 Eventually audits 4 Check configuration after rebooting 5 Termination 6 Termination + File access control Renewal Safety Liveness 1 2 3 5 4 6

  37. Outline • Motivation • Which policies can monitors enforce? • Language for writing complex policies? • Delineating the enforceable policies • Formally define “monitor enforces a policy” • Analysis of enforcement powers • A language for writing complex policies • Handle complexity with composition • Polymer language • Summary

  38. Managing Complexity via Centralization Application with policyscattered throughout Application with centralized policy Policy contains: - Security code - When to run the security code Scattered policy is hard to find and reason about Centralized policy is easier to find and reason about

  39. Beyond Centralization: Composition • Policy centralization is not enough • Need methodology for organizing a complex centralized policy • Our language Polymer provides a flexible methodology for decomposing complex policies into simpler modules • Policies are first-class and organized for composition • Higher-order policies (superpolicies) can compose simpler policies (subpolicies)

  40. Outline • Motivation • Which policies can monitors enforce? • Language for writing complex policies? • Delineating the enforceable policies • Formally define “monitor enforces a policy” • Analysis of enforcement powers • A language for writing complex policies • Handle complexity with composition • Polymerlanguage • Summary

  41. Polymer Language Overview • Syntactically almost identical to regular Java source • Primary additions to Java • Key abstractions for first-class actions, suggestions, and policies • Programming discipline • Composeable policy organization

  42. First-class Actions • Action objects contain information about a method invocation • Static method signature • Dynamic calling object • Dynamic parameters • Policies can analyze actions about to be executed by the target • Policies can synthesize actions to invoke on behalf of the target

  43. Action Patterns • Action objects can be matched to patterns in aswitch statements • Wildcards can appear in action patterns aswitch(a) { case <void System.exit(int status)>: E; … } <public void java.io.*.<init>(int i, …)>

  44. First-class Suggestions • Policies return Suggestion objects to indicate how to handle trigger actions • IrrSug: action is irrelevant • OKSug: action is relevant but safe • InsSug: defer judgment until after running and evaluating some auxiliary code • ReplSug: replace action (which computes a return value) with another return value • ExnSug: raise an exception to notify target that it is not allowed to execute this action • HaltSug: disallow action and halt execution

  45. First-class Suggestions • Suggestions implement the theoretical capabilities of monitors • IrrSug • OKSug • InsSug • ReplSug • ExnSug • HaltSug Different ways to accept Insert Different ways to suppress

  46. First-class Policies • Policies include state and several methods: • query() suggests how to deal with trigger actions • accept() performs bookkeeping before a suggestion is followed • result() performs bookkeeping after an OK’d or inserted action returns a result public abstract class Policy { public abstract Sug query(Action a); public void accept(Sug s) { }; public void result(Sug s, Object result, boolean wasExnThn) { }; }

  47. Compositional Policy Design • query() methods should be effect-free • Superpolicies test reactions of subpolicies by calling their query() methods • Superpolicies combine reactions in meaningful ways • Policies cannot assume suggestions will be followed • Effects postponed for accept() and result()

  48. A Simple Policy That Forbids Runtime.exec(..) methods public class DisSysCalls extends Policy { public Sug query(Action a) { aswitch(a) { case <* java.lang.Runtime.exec(..)>: return new HaltSug(this, a); } return new IrrSug(this); } public void accept(Sug s) { if(s.isHalt()) { System.err.println(“Illegal exec method called”); System.err.println(“About to halt target.”); } } }

  49. Another Example:public class rebootPolicy extends Policy public Suggestion query(Action a) { aswitch(a) { case <void ex.SubSys.reboot( )>: if(needToCheck) return new HaltSug( ); else if(aboutToInsert) return new OKSug( ); else return new ReplSug(null); case <* ex.SubSys.checkConf( )>: if(needToCheck) return new InsSug(rebootAct(a)); else return new IrrSug( ); default: if(needToCheck) return new HaltSug( ); else return new IrrSug( ); } } private boolean needToCheck = false; private boolean aboutToInsert = false; public void accept(Sug s) { if(s.isReplace( )) needToCheck = true; else if(s.isInsert( )) { needToCheck = false; aboutToInsert = true; } else if(s.isOK( )) aboutToInsert = false; }

  50. Policy Combinators • Polymer provides library of generic superpolicies (combinators) • Policy writers are free to create new combinators • Standard form: public class Conjunction extends Policy { private Policy p1, p2; public Conjunction(Policy p1, Policy p2) { this.p1 = p1; this.p2 = p2; } public Sug query(Action a) { Sug s1 = p1.query(a), s2 = p2.query(a); //return the conjunction of s1 and s2 …